The Effects of Integration Strategies on Firm Performance...

MSc. in Finance and International Business Author: Selen Gül

Department of Business Administration Advisor: Valerie Smeets

The Effects of Integration Strategies on Firm Performance

An Empirical Study on Danish Manufacturing Firms

Abstract:

The firms’ diversification strategy choices and their impact on corporate performance have been the center of attention both empirically and theoretically in the fields of strategy and finance for more than 30 years. However in general, previous studies have analyzed the integration-performance relationship without differentiating the industries that the firms were operating in, but rather the samples were pooled across industries. The aim of this paper is to investigate the performance effects of vertical, horizontal, unrelated integration and un-diversification strategies, by using a sample of 147 Danish manufacturing companies distinguished among 5 large industries, through the years 2009 to 2005. Empirical evidence shows that horizontal (related) integrated companies are outperforming the corporate performance of unrelated diversified firms, and the structure of the market, the level of concentration have varying effects on performance for each type of industry. Out of 5 industries, the manufacture of food products has the highest average performance measure, and the empirical results underline the significant and positive effect of the horizontal integration strategy for the manufacture of food products and manufacture of machinery and equipment industries that were subject to be tested.

August 2011

Aarhus School of Business, Aarhus University

Selen Gül The Effects of Integration Strategies on Firm Performance

1

Table of Contents

1. Introduction ................................................................................................................ 3

1.1.Research Questions............................................................................................ 4

1.2.Structure of the Thesis ....................................................................................... 5

2. Literature Review ........................................................................................................ 6

2.1.Theories of Vertical Integration ........................................................................ 6

2.1.1. Make or Buy Decision ..................................................................... 6

2.1.2. The Transaction Cost Theory .......................................................... 7

2.1.3. The Property Rights Theory ............................................................ 8

2.1.3.1.Benefits and Costs of Contracts........................................... 9

2.1.4. The Theory of Relational Contracts ................................................ 10

2.1.5. Is Vertical Integration Beneficial for the Firm? .............................. 10

2.1.6. Empirical Evidence on Vertical Mergers ........................................ 11

2.2.Horizontal Integration ........................................................................................ 12

2.2.1. Economies of Scale and Scope ........................................................ 13

2.2.2. The Learning Economy ................................................................... 14

2.2.3. Empirical Evidence on Horizontal Mergers .................................... 15

2.3.Diversification ................................................................................................... 16

2.3.1. Product Diversification .................................................................... 17

2.3.2. Geographic Diversification .............................................................. 17

2.3.3. The Determinants and Motives for Diversification ......................... 18

2.3.4. The Resource-Based View .............................................................. 19

2.3.5. Diversification and Firm Performance ............................................ 20

2.3.6. Empirical Evidence on Diversification and Firm Performance ...... 22

3. Development of Hypotheses ........................................................................................ 24

4. Methodology ................................................................................................................. 26

5. Data Construction........................................................................................................ 28

5.1.Sample Selection ............................................................................................... 28

5.2.Variables Measurement ..................................................................................... 30

5.2.1. Performance Measures (Dependent Variables) .............................. 30

5.2.2. Independent Variables ..................................................................... 31

5.2.3. Control Variables ............................................................................. 32


2

5.3.Limitations ......................................................................................................... 36

6. General Descriptive Analysis of Each Industry ........................................................ 37

6.1.Manufacture of Basic Pharmaceuticals and Pharmaceutical Preparations ........ 37

6.2.Manufacture of Food Products .......................................................................... 41

6.3.Manufacture of Chemicals and Chemical Products .......................................... 44

6.4.Manufacture of Furniture ................................................................................... 46

6.5.Manufacture of Machinery and Equipment ....................................................... 48

7. Industry Comparisons ................................................................................................. 51

8. Empirical Findings and Discussion of Results .......................................................... 53

8.1.Manufacture of Food Industry ........................................................................... 53

8.2.Manufacture of Machinery and Equipment Industry ........................................ 57

8.3.Discussion of Results......................................................................................... 60

9. Conclusion .................................................................................................................... 63

References ........................................................................................................................... 65

Appendices ......................................................................................................................... 72


3

1. INTRODUCTION

In this new era, where technological innovations are growing at a fast pace leading to

a more globalized world, corporations are facing a change in their form, structure and scope.

These new technologies engendered goods to be produced at lower costs, compared to what

organizations could achieve using older technologies. In order to benefit from these

production opportunities, firms require reliable supplies of inputs, access to widespread

distribution and retail outlets. Based on these necessities, the relationships among

manufacturers, their suppliers, and their distributors have been affected by this product line

and volume expansion.

In relation to this phenomenon, the question of the diversification-performance

relation, has been generally the most studied in the literature. The scholars’ main focus has

been on the value enhancing or destructive effects of diversification, and the conclusions vary

based on the perspectives of the studies that are conducted. Santalo & Becerra (2008)

underline that, while several authors have found strong evidence of trading at a discount for

diversified firms, supporters indicate that diversified firms are more productive compared to

stand-alone businesses. Moreover, the early contributions of Rumelt (1974) and Penrose

(1995) indicate that, as firms diversify into more unrelated areas, a lower performance

outcome is more likely.

Besides the effects of unrelated diversification and firm value, the companies may

initially choose to either vertically or horizontally integrate. Manufacturing firms increasingly

choose to vertically integrate; meaning that, rather than relying on independent suppliers,

factors and agents, they choose to produce the raw materials themselves and even distribute

finished goods. Moreover, new production technologies have given firms the opportunity to

exert scope economies by producing a wider range of products at a lower cost, compared to

be produced separately, leading them to horizontally integrate. (Besanko et. al, 2007)

Through diversification within their areas of business, the companies desire to reduce costs

and improve market effectiveness by utilizing economies of scale and scope.

Besides these integration strategies, geographic diversification plays a key role in the

strategic behavior of the large companies and their corporate performance. The company’s

expansion to different geographic locations as to different global regions and countries would

define international diversification (Hitt et. al, 1997) Its importance comes from the utilization

of the foreign market opportunities.

The research on diversification and firm value has focused primarily on US and

European based companies, without taking the performance effects of vertical and horizontal


4

integration into consideration. In addition, there are few studies that have focused on a single

country, as Kahloul & Hallara (2010) evaluated the performance effects of the French firms.

This paper will evaluate the performance measures by combining the impacts of unrelated

diversification and as well as vertical, horizontal integration strategies and remaining

undiversified. Moreover, in order to specify the results and overcome the socio-cultural

differences among countries, the main focus will be on Danish manufacturing companies and

the outcomes are to be evaluated based on five different industries.

1.1. Research Questions

Based on the definitions mentioned above, it is crucial to highlight the relationship

between firm performance and its level of integration strategies. By extending the study of

diversification-firm performance analyzers (Penrose, 1995; Rumelt, 1974; Bettis, 1981), the

aim of this paper is to question whether firms with an unrelated diversification, horizontal

integration, vertical integration or un-diversification strategy perform better or worse

compared to each other, and how these choices affect the firm performance. Prior studies

generally have taken the effect of integration strategies homogenous across the industries,

whereas this study investigates the effect of the strategies on performance by differentiating

the industries. This homogenous approach is neglected since different industries bear different

structural characteristics, which will lead to various average profits in each industry (Bettis &

Hall, 1982), and the type of concentration and competition within an industry are the leading

factors that orientate the companies to integrate or not (Penrose, 1995). The questions to be

addressed are as follows:

• What is the dominant integration strategy that each industry embraces and

which one has the highest affect on performance?

• How does the level of concentration change among the industries and does it

have a relation with the strategies chosen?

• Does the integration strategies have an impact on corporate performance and

do these effects differ based on the industries?

• Does the number of countries the firm is operating in, have an impact on firm

operating performance?


5

1.2.Structure of the Thesis

The next section will highlight the theoretical and empirical findings on the topic.

Section 3 develops the hypothesis based on the theoretical and empirical arguments

mentioned in the literature review. Section 4 gives in depth information of the methodology

used, and Section 5 describes the data collection procedure. Section 6 presents the summary

statistics for the industries involved in the study. Section 7 illustrates the comparisons among

these industries based on their summary statistics. Section 8 presents the empirical findings

and the discussion of the results, and finally, Section 9 makes concluding remarks regarding

the study.


6

2. LITERATURE REVIEW 2.1.Theories of Vertical Integration

Coase (1937) suggests that the introduction of the firm is initially based on the

existence of the marketing costs. The number of transactions or the activities of the firm

within its boundaries is the determining component in assessing the size of the firm, rather

than its output. These boundaries are defined as the vertical boundaries since these activities

are related at the various levels of the supply chain. Sudarsanam (2010) defines vertical

integration as “the combination of successive activities in a vertical chain under common

coordination and control of a single firm.” (p. 153) Vertical integration defines the activities

that the company performs within its boundaries, compared to the purchases from

independent firms in the market (Besanko et.al, 2007). In other words, vertical merger

replaces two or more independent firms with a single firm, and rather than relying on arm’s

length market-based transactions or contractual dealings, it internalizes the coordination of the

successive activities of the firm. Fan & Goyal (2006) indicate that vertical mergers procure

acquiring companies with ownership and control over contiguous stages of production. These

mergers allow firms to substitute internal exchanges within the boundaries of the firm for

contractual or market exchanges. Although vast amounts of theoretical studies on vertical

integration exist, there is inadequate number of empirical work on vertical mergers, and the

ones conducted are based on small samples.

2.1.1. Make-or-Buy Decision

Make-or-Buy decisions address the questions of: Why do some firms prefer a

vertically integrated structure, while others specialize in one stage of production and

outsource the remaining stages to other companies? In other words, should a firm produce its

own inputs, buy them in the spot market or preserve the relationship with a specific supplier.

This decision determines the firm's level of vertical integration, since every decision identifies

which operations the firm will engage in and which it will outsource from the suppliers

(Walker & Weber, 1984). This notion is concerned with the decision whether to integrate

backwards, which is “to internalize production of an input rather than source it from an

external supplier.” (Sudarsanam, 2010, p. 158) Therefore the ‘make’ part of the decision

emphasizes that ownership is joint and control rights are integrated, whereas under the latter,

they are separate. Moreover, the costs and benefits of either alternative have to be taken into

consideration. For instance, this choice may depend on a range of factors such as; “the current

and future availability of spot markets for arm’s length transactions, the cost of sourcing from


7

the spot market, the direct and indirect costs of contracts and informal arrangements,

uncertainty and information asymmetry between buyer and seller and indirect costs of

internalizing production.” (p.158)

Based on these factors, the company can choose to perform the activities in-house or

buy them from the specialists in the market that are called market firms (Besanko et. al,

2007). There are many advantages and disadvantages of using the market firms to source the

upstream activities in the vertical change. The benefits would be achieving scale and learning

economies, as well as efficient division of labor and specialization from the supplier’s side.

On the other hand, the downsides would be the issue in coordinating the production process,

the leak of private information, agency and influence costs, moral hazard and disincentives for

innovation.

2.1.2. The Transaction-Cost Theory

The transaction costs theory (TC) can be traced back to Coase (1937) who indicated

that the production will take place within the firm when the cost of organizing the production

through the market exchange is larger than within the firm. In other words, the firms may

avoid the costs of transacting with the market firms by carrying out the activity in-house. This

cost of transacting with independent market firms is defined by Coase (1937) as the cost of

using the price mechanism. The size of the firm will be based on the cost of using the price

mechanism, in which “a firm will tend to expand until the costs of organizing an extra

transaction within the firm become equal to the costs of carrying out the same transaction by

means of exchange on the open market or the costs of organizing in another firm.” (p. 395)

Leiblein & Miller (2003) argue that, although the applicants of the theory generally assume

that markets ensure a more efficient mechanism for exchange compared to the hierarchy, in

certain situations the costs of the market exchange may be too high and surpass these

efficiencies procured by the market. Therefore, the theory focuses on determining the features

of exchanges that are best suited to the firms and the market. Williamson (1975) indicates that

these inefficiencies originate from small numbers of bargaining situations. “Due to the

bounded rationality of decision-makers, the asymmetric distribution of relevant information,

and the inability to completely specify behavior in the presence of multiple contingencies, the

theory maintains that all contracts are incomplete and therefore subject to renegotiation and

the possibility of opportunistic behavior.” (Leiblein & Miller, 2003, p. 842) Opportunistic

behavior is more apparent, when an exchange demands one or more parties to get involved in


8

significant transaction-specific investments, which in turn create quasi-rents1 that, may lead to

hold-up2. Such relation-specific investment creates difficulty in switching to a new customer

due to the increases in costs, thus locking the supplier into that relationship (Sudarsanam,

2010). Besanko et al. (2007) and Sudarsanam (2010) are underlining the types of specificities

as; site, physical characteristics, dedicated assets and human assets specific.

Therefore, based on these downsides of contracts, vertical integration is thought to be

beneficial, where hold-up concerns are severe. Firms are expected to depend on in-house

production when the transactions are complex, specific investments are included, those

specific assets are unceasing, the quality of those assets are hard to be verified, the

environment is uncertain and when the quasi-rents based on the relationship are large.

2.1.3. The Property-Rights Theory

The property-rights theory, which has been developed by Grossman & Hart (1986),

emphasizes how asset ownership can change investment incentives. They propose two types

of contractual rights as; the specific rights and residual rights of control. “When it is too

costly for one party to specify a long list of the particular rights it desires over another’s

party’s assets, it may be optimal for that party to purchase all the rights except for those

specified in the contract.” (p. 692) The purchase of the residual rights of control is called

ownership. All the residual control rights of the physical assets in question are held by the

entity under integration, whereas under non-integration, the assets are owned individually

(Hubbard, 2008). Moreover, Grossman & Hart (1986) present that the allocation of residual

control rights to one party strengthens the investment incentives of that party, while

weakening the counter party’s investment incentives. “Integration shifts the incentives for

opportunistic and distortionary behavior, but it does not remove these incentives.” (p. 716)

Therefore, both costs and benefits from integration will exist. One of the concluding remarks

of Grossman & Hart (1986) is that, integration is suggested when one party’s investment

incentives is relatively more important to the other firm’s incentives. On the other hand, when

both investment decisions are equally and somewhat crucial, non-integration is preferable.

Compared to the TC literature, the PR literature does not underline the ex post

haggling, renegotiation and opportunistic behavior. “Instead it stresses contractual

incompleteness and develops formal models that show how ex post bargaining affects ex ante

investment in non-contractible assets.” (Lafontaine & Slade, 2007, p. 650) Kim & Mahoney 1 Quasi-rent would be “the extra profit that you get if the deal goes ahead as planned, versus the profit you would get if you had to turn to your next-best alternative.” (Besanko et. Al, 2007, p. 126) 2 The term hold-up will be explained more in detail under section 2.1.3.1.


9

(2005) further indicate the importance of property rights theory, as that various specifications

of property rights arise in response to the economic problem of allocating scarce resources,

and how it affects the economic behavior and economic outcomes in return.

2.1.3.1. Benefits and Costs of Contracts

According to the theories mentioned above, the existence of market failures may lead

the firms to source its inputs from suppliers by negotiating contracts. The duration of these

contracts may be short or long-term in nature. Williamson (1971) introduces three alternatives

to be considered: a life time contract, a series of short-term contracts, and vertical integration.

The once-for-all type of contracts are facing the dilemma of the redesign issues due to

changing technology, in which sequential decision process is needed. “If, however,

contractual revisions or amendments are regarded as an occasion to bargain opportunistically,

which predictably they will be, the purchaser will defer and accumulate adaptations, if by

packaging them in complex combinations their true value can better be disguised; some

adaptations may be forgone altogether.” (Williamson, 1971, p. 116) Therefore, short-term

contracts may be more preferable due to sequential decision making and adaptation. However,

the downsides would be the necessity of relation-specific investments and the existence of a

first-mover advantage for one of the parties (Williamson, 1971). These downsides would

generate the hold-up problem or behaving opportunistically, in which it occurs when one of

the parties would attempt to renegotiate the terms of the contract. The party that has been

held-up could be either the buyer or the supplier, but most likely the one that has engaged in a

relation-specific investment (Besanko et. al, 2007). In order to eliminate this hold-up problem,

Williamson (1971) suggests the firms to vertically integrate, in which the disadvantages of

long and short term contracts would be avoided. “Sequential adaptations become an occasion

for cooperative adjustment rather than opportunistic bargaining; risks may be attenuated;

differences between successive stages can be resolved more easily by the internal control

machinery.” (Williamson, 1971, p. 116)

Besides the solution of vertical integration, only a complete contract can eliminate

opportunistic behavior. Besanko et al. (2007) argue the applicability of complete contracts,

and underline that this type of contracts would be feasible only if the parties are able to

specify each contingency to be occurred and the set of actions to be taken. Therefore,

contracts in the real-world are incomplete, which involve some degree of open-endedness or

ambiguity. The literature on transactions costs highlights that incomplete contracts can cause

a non-integrated relationship to yield outcomes that is inferior compared to complete


10

contracts. The three fundamental factors preventing to achieve complete contracting are;

bounded rationality, difficulties specifying or measuring performance and asymmetric

information.

2.1.4. The Theory of Relational Contracts

In relation to this phenomenon of contracts, the third insight is formed by Baker et al.

(2002) indicating that “relational contracts are informal agreements and unwritten codes of

conduct that powerfully affect the behaviors of individuals within firms.” (p. 39) These

relational contracts affect the behaviors of firms in their business relations with other firms,

whether vertical or horizontal. Baker et al. (2002) underline in their study the ease of

relational contracts between and within the firms, compared to the difficulties encountered in

formal contracting. “For example, a formal contract must be specified ex ante in terms that

can be verified ex post by the third party, whereas a relational contract can be based on

outcomes that are observed by only the contracting parties ex post, and also on outcomes that

are prohibitively costly to specify ex ante.” (p. 40) Therefore, a relational contract empowers

the parties to exploit their detailed knowledge to their particular situation and to adapt this

situation to new information as it becomes available. Based on these advantages of relational

contracts, the authors are adding dynamics to the previous models and illustrate how these

dynamics will affect the vertical integration decisions by introducing game theory models

such as; trust games, repeated trust games and trigger strategies.

2.1.5. Is Vertical Integration Beneficial for the Firm?

According to Sudarsanam (2010), vertical integration increases technical efficiencies

in some ways; however arises inefficiencies in some other ways. The author describes these

technical efficiencies as coordinating, monitoring, and enforcement in the process of

production. On the other hand, interdivisional rivalry may lead to opportunism and an

increase in influence costs. Moreover, information asymmetry in integrated firms may exist

between various levels of management and divisions. “In particular, a firm that purchases its

supplier, thereby removing residual rights of control from the manager of the supplying

company, can distort the manager's incentives sufficiently to make common ownership

harmful.” (Grossman & Hart, 1986, p. 692) When the residual rights are captured by one

party, they are lost for the contrary party that may lead to distortions. On the other side, by

vertically integrating no alternative use of the good will exists, leading to a value of zero

quasi-rent and no hold-up problems (Williamson, 1971).


11

2.1.6. Empirical Evidence on Vertical Mergers

The efficiencies of vertical integration have been subject to be tested by several

scholars in order to illustrate why firms take parts production in-house and what types of

specificities are affecting vertical integration (Monteverde & Teece, 1982; Masten, 1984) ,

and how the duration of the contracts are affecting the choice to vertically integrate (Joskow,

1985). Monteverde & Teece (1982) have explained vertical integration by examining the U.S.

automobile industry for the two firms, GM and Ford. The study observed a significant and a

positive effect on the engineering effort and specificity coefficients, meaning that a high level

of engineering effort and the specificity of the component will more likely lead the

component to be produced in-house. “GM and Ford are more likely to bring component

design and manufacturing in-house if relying on suppliers for preproduction development

service will provide suppliers with an exploitable first-mover advantage.” (p. 212) Moreover

Masten (1984) has followed a similar approach by analyzing the variables on vertical

integration by using a sample from the U.S. aerospace industry of 1,887 aerospace

components. The author has found a significant positive effect for specialization and

complexity coefficients, in which the higher the complexity and specialization of the inputs,

the higher the probability to vertically integrate. In addition, Joskow (1985) has conducted a

study by examining the U.S. coal-burning electric generating plants in order to identify the

role of contract duration on vertical integration decisions. The author points out that the

variation in the contract duration is based on the level of relation-specific investments, in

which longer commitments are engaged where relation-specific investments are more

important. Moreover, in the studies of Fan & Goyal (2006), the authors give the basic idea of

a vertical merger as, the two industries are vertically related if one of the firms uses the

other’s output for its own production or if the firm can supply its product or services as the

other’s input. This measure can be captured by Input-Output tables and is applicable to

measure the vertical relations in large samples. Therefore, where merging firms are from the

same Input-Output industries, the merger is categorized as vertical.

Moreover Sudarsanam (2010) specifies that the empirical evidence on vertical mergers

and their value effects is rare, compared to the ones that have analyzed horizontal and

diversifying mergers. Colangelo (1995) has studied the effect of pre-emptive merging for

vertical vs. horizontal integration and underlined that the overall gain from a vertical

integration is generally greater than that from a horizontal integration. “In our context vertical

integration gives rise to three different gains: (a) it eliminates double marginalization; (b) it


12

enables price discrimination against non-integrated rivals; (c) it avoids the loss coming from

being non-integrated after a horizontal merger.” (p. 324) In addition, the findings of Leiblein

& Miller (2003) regarding the semiconductor industry point out that, the vertical boundary

choices are affected significantly depending on the firm-level competences and strategies. For

instance, the companies with greater experience in a specific type of technology have the

tendency to internalize the manufacturing activities than firms without such production know-

how. “Similarly, firms with high levels of sourcing experience are more likely to outsource

their production than firms that do not have such experience.” (p. 854) To sum up, firms

internalize transactions when it is expected that they will need to renegotiate supplier

contracts due to high asset specificity.

2.2.Horizontal Integration

Besanko et al. (2007) indicate that a firm’s horizontal boundaries determine the

quantities and varieties of products and services that it produces. It refers to a merger of two

or more firms producing the same good under one consolidated firm (Chakravarty, 1998).

Horizontal boundaries vary obviously across industries and across the firms within them. The

optimal horizontal boundaries of the firms are appertaining crucially to economies of scale

and scope. Economies of scale and scope exist whenever large-scale production, distribution,

or retail operations have a cost advantage over smaller operations. “Economies of scale and

scope not only affect the sizes of the firms and the structure of markets, but they are also

central to many issues in business strategy.” (Besanko et al., 2007, p. 75) Economies of scale

and scope are the essence for merger and diversification strategies. They have an effect on

entry and exit, pricing, and the capability of the firm to protect its long-term sustainable

advantage.

Sudarsanam (2010) underlines that, a number of firms in wide-ranging sectors such as

utility, electricity, banking, pharmaceuticals, insurance, oil and gas, automobiles, food and

drinks, steel and healthcare have merged with one another, in the recent years. Such mergers

are defined as horizontally related mergers. Where the firms selling the identical product

merge, it is described as a pure horizontal merger. “Where firms selling products that are not

identical in terms of end use but nevertheless share certain commonalities, such as

technology, markets, marketing channels, branding or knowledge base, merge, we refer to

such mergers as related mergers.” (p.123) For simplicity, Sudarsanam (2010) refers to the


13

term horizontal merger as to both pure horizontal mergers and related mergers3 of firms

selling a range of similar products. Horizontal mergers often qualify industries and markets

whose products are generally in the mature or declining stages of the production life cycle.

These markets have a low overall growth rate, and firms have accumulated production

capacity that far exceeds the demand. This combination of low market growth and excess

capacity engenders difficulties on firms to attain cost efficiencies through consolidating

mergers. Such efficiencies may be achieved from scale, scope and learning economies.

2.2.1. Economies of Scale and Scope

The origin of costs may have crucial inferences for industry structure and the behavior

of the companies. Besanko et al. (2007) denote that “the production process for specific good

or service exhibits economies of scale over a range of output when average cost declines over

that range.” (p.75) Moreover, economies of scale exist if the firm attains unit-cost savings as

it raises the production of a given good or service. In order to achieve these scale economies,

the associated costs, risks and the extent of cost savings have to be taken into notice

(Sudarsanam, 2010). Therefore, firms should be conscious about diseconomies of scale,

which arise from complexities of monitoring, diffusion of control, ineffectiveness of

communication, and numerous layers of management. In addition to these disadvantages,

Besanko et al. (2007) also underline the limits to economies of scale, in which beyond a

certain size, bigger is no longer better and may even lead to worse outcomes. The most

important reasons for these limits are; labor cost and firm size, conflicting out, spreading

specialized resources too thin, and incentive and bureaucracy effects. Moreover, economies of

scale may be more crucial for the manufacturing organizations, “since the high capital costs of

plant need to be recovered over a high volume of output.” (Johnson et al. 2008, p. 99) The

manufacturing sectors that have been generally important have been motor vehicles,

chemicals and metals. In terms of distribution and marketing other industries such as drinks,

tobacco and food, the scale economies would be crucial (Johnson et al. 2008).

Economies of scope exist, if an increase of production in the variety of goods and

services saves the firm from the costs it bears. “Whereas economies of scale are usually

defined in terms of declining average cost functions, economies of scope are usually defined

in terms of the relative total cost of producing a variety of goods and services together in one

firm versus separately in two or more firms.” (Besanko et al., 2007, p. 76) In other words,

Panzar & Willig (1981) point out to the existence of economies of scope where it is less

3 This paper will handle related diversification under the term ‘horizontal integration’.


14

costly to merge two or more product lines in one firm compared to supplying them separately.

Based on the definitions above, scope economies are available only for multi-product firms.

Certainly, both economies may be recognized by the increase of the output of individual

products as well as the total output of all the firm’s products. The research on the extent of

scope economies is scarce, in contrast to the literature on scale economies. One possible

explanation is that until recently product costing did not allocate costs to the different

products correctly, based on the related activities. Activity-based costing (ABC) mitigates this

issue; however the problem of how to compare these product costs in the merged firm with

the costs on the similar products produced separately by different companies still exists

(Sudarsanam, 2010).

2.2.2. The Learning Economy

Experience is an important determinant to fulfill the tasks faster and attain the output.

The magnitude lies under the idea of the learning curve. Besanko et al. (2007) determine that

economies of scale points out to the advantages that flow from increase in production to a

larger output at a given point in time. “The learning curve refers to advantages that flow from

accumulating experience and know-how.” (p. 94) Sudarsanam (2010) specifies that the

economy of learning comes to light when workers and managers become more experienced

and effective over time in using the available resources of the firm, and help decrease the cost

of production. “The time required to do a job will decrease each time the job is done, that the

time per unit will decrease at a decreasing rate, and that the time reduction will be

predictable.” (Lindsey & Neeley, 2010, p. 73) It is a function of cumulative output over

several periods, and increasing cumulative output raises the motivation to learn more efficient

and effective ways of producing each unit of the output for the managers and workers.

Employees learn not only from their personal experience but also from that of their

colleagues. The limit to learning and its affect on cost reduction is designated by the minimum

efficient learning scale (MELS). At this level, maximum learning has been procured (Besanko

et al., 2007).

Based on the studies conducted, the semiconductors and aircraft production are some

of the industries that the learning economy may be more crucial. The learning rates averaged

about 20 to 40 percent respectively. Learning curve efficiency entails that the firms have a

large sales quantity and therefore a relatively large market share. “Therefore, the cost of

acquisition of the increased market share needs to be balanced against the subsequent cost

savings from increased learning efficiency.” (Sudarsanam, 2010, p. 138) Moreover, Besanko


15

et al. (2007) emphasize that learning occurs at different rates for different organizations and

processes, according to the variation in slopes across firms and products. Although

organizational learning is highlighted as the essence of the process, primarily it is individuals

who learn. While individuals do the learning, the firms can take the steps to enhance learning

and the maintenance of knowledge in the organization.

Horizontal mergers lead to the consequence of a sudden increase in the quantity of

output when the output of each merging firm is combined. While each firm has the

opportunity to learn from the experience of the other firm, this learning may not engender the

cumulative output of the merged entity to increase more. In the period subsequent the merger

this output may increase, hence creating opportunity for further learning. However, if the

output of the merged company is already large, it is expected to have passed the minimum

efficient learning scale (MELS) of cumulative output (Sudarsanam, 2010). For instance,

“mergers involving complex technological processes such as drug discovery may yield

potentially valuable learning opportunities, but they are also problematic because of the

coordination and management problems.” (p. 139)

2.2.3. Empirical Evidence on Horizontal Mergers

Lipczynski et al. (2005) signify that the empirical evidence on the increased

profitability through increased market power or cost savings of horizontal mergers is rather

conflicting and inconclusive. For instance, Cosh et al. (1980) examine 211 mergers in the UK

between the years 1967 and 1969, comparing profitability during a five-year period before the

merger, with profitability during the five years subsequent the merger. The merged firms are

observed to have experienced an increase in average profitability. On the contrary, Meeks

(1977) detects a fall on average profitability during the seven-year period following the

merger in a study of mergers in the UK between 1964 and 1972. In addition to these studies,

Ravenscraft & Scherer (1987) examine the pre-merger profitability of 634 US target firms in

the late 1960s and early 1970s. The target firms’ profitability (the ratio of operating income to

assets) was observed to be 20 percent, which is much greater than the average profitability of

all firms of 11 percent.

Moreover, Weiss (1965) inspects the impacts of horizontal mergers on seller

concentration for six manufacturing industries for the period 1926-1959. “Changes in

concentration ratios over approximate 10-year intervals are decomposed into effects arising

from the internal growth of firms, the exit of incumbent firms, mergers, and turnover or

changes in the identity of the largest firms in each industry.” (Lipczynski et al., 2005, p. 263)


16

Therefore, internal growth and exit seem to have a more crucial role than mergers in affecting

the changes in concentration.

Finally, Colangelo (1995) underlines the gains from horizontal integration as: it leads

to an increase in the market power due to the internalization of the cross-price effect on

demand, and it prevents the loss coming from being non-integrated after a vertical integration.

2.3.Diversification

The incentive and consequences of diversification on firms has been committed to a

vast amount of studies by both economists and business researchers. However, these two

groups approached the phenomenon from different perspectives. “Economists have treated the

extent of a firm’s diversification as determined by structural variables in the industries in

which the firm operated and the economics of the organization of activity within the firm

operated and the economics of the organization of activity within the firm compared to via the

market.” (Lecraw, 1984, p. 179) On the other hand, business researchers have paid attention

on the human and physical assets of the firm, by taking its internal strengths and weaknesses

into consideration in determining its diversification strategy. This paper will have the focus of

the economists’ perspective in identifying the companies’ diversification strategies, in which

the structural variables of the industry and the activity of the firm within this industry will be

highlighted.

Lipczynski et al. (2005) define a diversified firm or a conglomerate as; to being

involved in the production of a number of various goods and services, making it a multi-

product firm. According to the authors, the types of diversification can take the forms as

product extension, market extension and pure diversification. Product extension would be

achieved if a firm can diversify by producing a new product that is strongly related to its

existing products. Market extension involves diversifying into a new geographic market with

the same line of products, and a pure diversification strategy involves a transition into

unrelated areas of business activity. Rumelt (1982) depicts the first and the last components of

the strategies respectively as related4 and unrelated business companies.

Lipczynski et al. (2005) further indicate two ways in which a diversification strategy

can be performed; either through internally generated expansion, or through mergers and

acquisitions. “Conglomerate merger involves the integration of firms that operate in different

product markets, or in the same product market but in different geographic markets, whereas

internally generated expansion is likely to require the simultaneous extension of the firm’s

4 Recall that this paper takes “related diversification” strategy under the name of horizontal integration.


17

plant and equipment, workforce and skills base, supplies and raw materials, and the technical

and managerial expertise of its staff.” (p. 593) Diversifying through a conglomerate merger

may be less demanding in this matter.

2.3.1. Product Diversification

As indicated above, the strategies of related and unrelated integration are defined

under product diversification. Although this paper will refer to the concepts as horizontal

integration and unrelated integration strategies, it is worth mentioning this broad definition

and its performance effects. Ravichandran et al. (2009) notes that, product diversification

which illustrates the scope of the multiple and distinct product markets that the firm is

operating in, has been lately under the focus of strategic management researchers. Geringer et

al. (2000) indicate “the relationship of performance and the product mode of diversity is well

established by studies in two related directions — type of diversification and degree of

diversity.” (p. 54) Rumelt (1974) found differences across his “relatedness” categories, in the

seminal study of qualitative types of diversification. The author divided the integration

strategies into 7 categories; which were single business, dominant vertical, dominant

constrained, dominant linked-unrelated, related constrained, related linked and unrelated

business. In order to specify the strategy that a company possesses, Rumelt (1974)

constructed intervals of ratio specification. Based on these intervals of ratios (specialization

ratio, related-core ratio, related ratio and vertical ratio) the companies’ strategies were

specified. Following studies using his methodology have generally underlined that related

diversification generated higher performance levels than unrelated diversification, although

industry effects and other firm-level variables tend to eliminate much of the effect of the

diversification type. Therefore, the general outcome of the studies is that related

diversification is associated with a profitability advantage (Geringer et al., 2000).

2.3.2. Geographic Diversification

Geographic diversification is identified as the firm’s expansion into various

geographic locations or markets across the borders of regions and countries (Hitt et al., 1997).

“Thus, a firm's level of international diversification is reflected by the number of different

markets in which it operates and their importance to the firm (as measured, for instance, by

the percentage of total sales represented by each market).” (p. 767) This type of

diversification strategy has its motivations as well as downsides. Denis et al. (2002) identify

several motivations as; global diversification is a mechanism that combines the information-


18

based assets of buyers and sellers within the same firm. It generates value by creating

flexibility within the firm, by giving the ability to respond to changes in relative prices. In

addition, investors’ diversification choices can result as the benefit of geographic

diversification. Ravichandran et al. (2009) adds the scope and scale economies, enhanced

market power, and the ability to supply lower-cost factor inputs to the benefits of global

diversification. Moreover, “increased operational flexibility by global diversification reduces

the risks across the markets.” (Kim & Mathur, 2008, p. 749) However as from the downside

perspective, a globally diversified entity is more complex compared to a purely domestic

firm. The costs of information asymmetry between corporate headquarters and the difficulty

of monitoring managerial decision-making may give rise (Denis et al., 2002).

Based on the empirical studies conducted, Ravichandran (2009) and his colleagues

specify that, “multinational corporations (MNCs) experienced a positive valuation effect

relative to purely domestic firms because of their role as financial intermediaries.” (p. 210)

Moreover, Lepetit et al. (2004) illustrate that the announcements of the mergers and

acquisitions beyond regions and countries have a positive effect on the market. On the other

hand, the effect on firm performance may be negative due to high transaction costs and

managerial-information processing demands. Moreover, Delios & Beamish (1999) have

found a positive relationship between the geographic scope and firm’s performance by

collecting a data of 399 Japanese manufacturing firms. Their findings illustrate that expanding

into new geographic markets is an effective strategy for developing the performance of

Japanese companies. However, in the study of Kim & Mathur (2008) where a sample of

28,050 firm year observations from 1990 to 1998 was used, a firm value decrease was

associated for both industrial and geographic diversification. “We find that geographically

diversified firms have higher R&D expenditures, advertising expenses, operating income,

ROE and ROA than those of industrially diversified firms.” (p. 764)

2.3.3. The Determinants and Motives for Diversification

In exploring the determinants of diversification, Rondi et al. (1996) focuses on three

theories of diversification. The first, attributed to Marris (1964) and Penrose (1995), propose

that the managers seek to maximize the growth of the firm. The operation of specific assets

such as marketing skills and technical enterprise in other industries offers a convenient

vehicle in order to achieve the growth objective. The second theory attributed to Bain (1959),

puts emphasis on the conditions that yield entry possible or attractive. These incorporate

industry-level characteristics such as growth and concentration, average profitability, as well


19

as barriers to entry. The third theory, attributed to Rumelt (1974) and Williamson (1975),

focuses on relatedness between industries that makes diversification attractive, in which

relatedness refers to the similarities between markets, technologies, and organizational

structures (Lipczynski et al., 2005). The scope of this paper generally refers to the third theory

where relatedness is the underlying concept.

As mentioned above under the heading of horizontal boundaries, related

diversification represents the horizontally integrated mergers. Therefore, this part will

consider the value creation for the acquisitions of unrelated businesses. Sudarsanam (2010)

underlines the motives of value creation as having an increased market power or operating an

efficient internal capital market. “Market power is the ability of a firm in a market to pursue

anticompetitive behavior against its current rivals or potential entrants.” (p. 184) This power

is not obtained from the monopoly position in that market, but from the range of the firm’s

activities and its size. Based on this market power, the conglomerates assign investment funds

to a wide range of individual entities. If these entities were stand-alone, independent firms,

their funds would be supplied directly from the capital markets. Thus, the conglomerate firm

serves the role of capital markets. The firm will create value, in case it possesses an effective

performance compared to the external capital market. Moreover, Lipczynski et al. (2005)

add more motives such as; saving costs, reduction of transaction costs and the managerial

motives for diversification.

2.3.4. The Resource-Based View

A vast amount of the management literature on diversification follows the resource-

based view of the firm. “The resource view argues that rent-seeking firms diversify in

response to excess capacity in productive factors, here called resources.” (Montgomery, 1994,

p. 167) Under this perspective, firms acquire companies to keep the balance among the

required competitive profile and competences, and their current endowments of resources.

However, the amount of resources available are limited, therefore firms are not limitless in

their ability to pursue new investment opportunities (Wiersema & Bowen, 2008). Apart from

this limitation, conglomerate acquisition may be undertaken by the same motives for

acquiring competitive profile and competences. Other reasons may be the need for growth,

and to utilize the excess capacity the firm possesses. These idle resources should be reused in

more productive and profitable areas. Therefore the question to be answered is, how best the

firm can exploit these resources outside of its current operations. In the book of Silverman

(2002), three sets of factors are pointed out as the firm’s diversification behavior. Initially is


20

the specific range of applications to which the firm’s current resources may be useful. These

depict the possible set of businesses in which the firm’s resource base will provide

competitive advantage. The second is the scope of transaction costs in the relevant markets

for the firms existing resources. These determine the firm’s ability to exploit its resources

through contractual arrangements, which can prevent the need for expansion of the firm’s

boundaries. The third set of factors deal with the sustainability of the competitive advantage

furnished by the firm’s resources. For the reason of prioritization, a firm will decide on to

focus first on the exploitation of those resources that offer the most sustainable competitive

position, since it cannot use all of its resources at once. Finally, “in order to generate

sustainable competitive advantage, it has been argued that firms’ resources and capabilities

should be rare, valuable, difficult to imitate, non-substitutable and non-transferable in that

they cannot be easily purchased in resource markets.” (Matraves & Rondi, 2007, p. 38)

2.3.5. Diversification and Firm Performance

Firm diversification has been extensively researched both empirically and

theoretically in the fields of strategy and finance for more than 30 years. The literature on

diversification generally focuses on the economic rationale behind the diversification-

performance relationship, and the main common objective of this work has been to verify the

effect of diversification on the creation or destruction of firm value. Thus, the researches’

center of attention has been on the performance of the diversified firms compared to

specialized firms (Santalo & Becerra, 2008).

Many researchers have studied the effects of operating performance on diversified

firms compared to undiversified, which is measured by accounting profits or productivity.

They have found the relationship between performance and corporate diversity to be

ambiguous. “Profits were more likely to be determined by industry profitability, coupled with

how the firm related new businesses to old ones, rather than diversification per se.”(Besanko

et al., 2007, p. 180)

Ravichandran et al. (2009) specify that firms may choose to diversify into related or

unrelated markets, based on the similarity or relatedness of the new business. “Related

diversification is believed to lead to better performance than unrelated diversification because

the former leverages significant business synergies while the latter suffers from agency costs

and inefficient resource allocation.” (p. 206) This belief has been widely studied by many

scholars. Prahalad & Bettis (1986) explain this logic more in depth, by indicating the four

major and nine minor categories that Rumelt (1982) has used to identify the diversification


21

strategies of the firms. The major categories are to be single business, dominant business,

related business and unrelated business. Rumelt (1982) has used statistical models to relate

diversification strategy to performance and pointed out that capital productivity is greater in

moderately diversified firms. However the firms in between moderate and high levels of

unrelated diversification acquired moderate or poor productivity. In other words, on the

average related diversification strategies outperformed the other diversification strategies. On

the other side, the unrelated business strategy was observed to be the lowest performing

(Prahalad & Bettis, 1986, p. 486). Moreover, “Noel Capon (1988) and his colleagues found

that firms that restricted their diversification to narrow markets performed better than did

broader firms, presumably because of their learning particular market demands.” (Besanko et

al., 2007, p. 180) Although the theoretical and empirical findings on the area of diversification

are quite rich, the results have not been consistent. Despite the inconclusive results,

diversification has been an effective firm strategy for growth (Ravichandran et al., 2009).

Lately, Nathanson & Cassano (1982) conducted a statistical study of diversity and

performance with a sample of 206 firms through years 1973 and 1978. They utilized two

factors which are market and product diversity to distinguish the diversification strategy that

improves Rumelt’s categories. The findings illustrated that, an increase in product diversity

decreased the average returns, whereas the returns remained rather stable for an increase in

market diversity. Also, they discovered that size plays a crucial role on the relationships. “For

both the market and product diversity, smaller firms did well relative to larger firms in

categories marked by no diversification and in categories of extremely high diversification,

and larger firms did significantly better than smaller firms in the in-between categories—

those characterized by intermediate levels of diversification.” (Prahalad & Bettis, 1986, p.

486) In both these studies of Rumelt (1982) and Nathanson & Cassano (1982), the key point

is to decide on the generic strategy of diversification (the level of relatedness) in order to

achieve performance (Prahalad & Bettis, 1986). According to this phenomenon Kiker &

Banning (2008) support that, diversification is an issue of creating fit with the most

significant contingencies and an effective fit will improve the overall performance of the firm.

According to this view, diversification does not necessarily lead to increased overall firm

performance; rather it relies upon how effective the diversification fits the particular

contingencies of the firm. “Research from this perspective has generally found that this occurs

to the extent that firms diversify only in a direction which is related to their core

competencies.” (Kiker & Banning, 2008, p. 20)


22

2.3.6. Empirical Evidence on Diversification and Firm Performance

A vast number of researches have been conducted in order to examine the relationship

between diversification and firm performance, by utilizing industry structure variables like

concentration, scale, industry growth, etc. In these studies, accounting indices, such as return

on equity or return on invested capital have been generally used to measure performance. The

common measure for diversification has been the Herfindahl index; for instance, one minus

the sum of the squared percentages of a firm's total revenues in each of its markets. “These

studies nearly always find a neutral or negative, not a positive, relationship between

diversification and firm performance.” (Montgomery, 1994, p. 169) Montgomery &

Wernerfelt (1988) and Lang & Stulz (1994) presented a similar analysis using Tobin's q, from

the perspective of the stock price performance (the capital market value of the firm divided by

the replacement value of its assets) to measure performance. Their findings illustrated a

reduction on the firms’ profitability as the level of diversification increased (Montgomery,

1994). In other words, “highly diversified firms are consistently valued less than specialized

firms.” (Lang & Stulz, 1994, p. 1278)

Schoar (2002) has examined the effect of productivity on firm performance and found

that diversification has caused to a destructive ‘new toy’ effect. “While the newly acquired

plants increase their productivity by three percent, incumbent plants show productivity

declines of almost two percent.” (p. 2380) This study is also supported by Lichtenberg (1992)

who underlines the fall of the productivity of plants as the level of diversification increases

(Schoar, 2002).

Ravichandran et al. (2009) focused on the effects of IT technology spending to

product and geographic diversification and firm performance. They have defined the firm

profitability by using the accounting-based measure of return on assets (ROA) and the

measure of Tobin’s q for firm valuation. The authors’ concluding remarks were; IT needs to

be viewed as a valuable asset by the managers in highly diversified firms, based on the

performance critical role when implemented with diversification. However, they must be

attentive that the impacts on performance are dependent on types of diversification. “In firms

with unrelated product diversification or with high geographic diversification, IT may not

contribute to performance as much as in related diversifiers and in low geographic

diversification contexts.” (p. 233) These findings are also supporting the work of Miller

(2006), which specifies the greater value creation from technological diversity of the multi-

business firms compared to single-segment firms, and the greater performance of diversified

as technological diversity increases.


23

Another perspective in assessing the performance-diversification linkage is

highlighted in the study of Santalo & Becerra (2008), in which this linkage is examined by

differentiating the industries. Their evidence illustrates that the effect of diversification on

performance is not homogenous across industries. “Diversified firms observe a diversification

discount if and only if they compete in industries with a large number of single-segment

companies or, equivalently, when specialists hold a considerable market share.” (p. 851) On

the other hand, industries that bear only a few non-diversified firms competing, leads the

diversified firms enjoy a premium in those industries in which specialists acquire a small

market share.

In addition, Bettis (1981) has conducted a study using a sample of 80 firms, in order to

investigate the performance differences between related and unrelated diversified firms. By

regressing the return on assets to advertising, R&D, plant investment, size, risk and

diversification strategy, the author concluded that, on average related diversified firms

perform better than unrelated diversified firms by about one to three percentage points of

return on assets.

Moreover, Denis (2002) and his colleagues examined the effects of global and

industrial diversification on the firm value, by using a sample of 44,288 firms through years

1984 and 1997. The findings highlight an increase in global diversification over time, whereas

a reduction for industrial diversification. However both global and industrial diversification is

associated with valuation discounts, which are statistically significant compared to purely-

domestic firms. Moreover, the authors have found no evidence of tendency to replace the

global diversification strategy for industrial diversification by the individual firms.

Finally, Capar (2009) examined a sample of 196 firms through years 1995-2000,

based on the effects of international and product diversification on innovation assets and firm

performance. The results are found to be significant for the effects of international

diversification on innovation assets and a negative effect for an increase of product

diversification. Therefore, “the present study provided strong evidence that innovation assets

lead to higher performance.” (p. 6)


24

3. DEVELOPMENT OF HYPOTHESES

Based on the prior research on the effects of the integration strategies on firm’s

performance, this section outlines the hypotheses that are subject to be tested. This paper will

focus on the two industries (manufacture of food products and manufacture of machinery and

equipment) out of the 5 industries in computing the regression models, due to the

adequateness of the amount of data. In order to examine if the results obtained will be related

to the previous studies, two separate hypotheses have been developed for manufacture of food

products and manufacture of machinery and equipment industries respectively.

Bettis & Hall (1982) underline the importance of the differences among industries.

Since this paper is analyzing the effects of the integration strategies by differentiating the

industries that the firms compete in, it is crucial to note that “the different industries have

different structural characteristics (in the industrial organization economics sense), and these

different structural characteristics result in different average (and potential) profits in each

industry.” (Bettis & Hall, 1982, p. 255) In relation to this phenomenon, it is expected that the

effects of the integration strategies on firm’s profitability may vary among the industries.

Moreover, Besanko et al. (2007) ask the question of whether they will encounter considerable

differences in profitability of business units within industries and a modest variation in

profitability across the industries. “If so, the effect of the market environment on profitability

is unimportant, but the effect of a firm’s competitive position in the industry is important.” (p.

349) The question can be asked vice versa, and the authors conclude that both the market and

the firm’s competitive position in the industry can explain profitability. McGahan & Porter

(1997) indicate that the industry is responsible for about 19 percent of the variation in profit

across industries, whereas the percentage is 32 for the business-specific effects.

In relation to these differences, the effects of the integration strategies can be tested

based on the theories presented above. Prior research indicates that, in order to prevent the

hold-up problem, firms tend to vertically integrate when their investment incentives are more

crucial compared to the counter party’s incentives (Grossman & Hart, 1986). Since the firms

tend to internalize their transactions in order to avoid the renegotiations of contracts and

investing huge amounts on the relation-specific assets, the following hypothesis is formed:

H1: Vertical integration strategies have a positive effect on firm’s performance.

The findings of the early studies of Rumelt (1982) designate that firms were different

not only in terms of their product diversity but also in the patterns of relationships they

created among various lines of businesses. Moreover, the different types of strategies of


25

diversification were associated with differing corporate profitability based on the strategy

chosen. “The highest levels of profitability were exhibited by those having a strategy of

diversifying primarily into those areas that drew on some common core skill or resource.” (p.

359) On the other hand, the lowest levels were those of vertically integrated businesses and

firms following strategies of diversification into unrelated businesses (Rumelt, 1982).

Besides the findings of Rumelt, the general empirical evidence has a strong support in

highlighting the related diversified firms are outperforming the unrelated diversified

companies (Montgomery, 1994; Lang & Stulz, 1994; Bettis, 1981). For instance, Chang &

Wang (2007) have examined a sample of 2,402 U.S. firms through years 1996 to 2002, and

found strong support that related product diversification leads to positive performance effects.

“Conversely, unrelated product diversification not only has a weaker influence than related

product diversification, it actually damages the performance of multinational firms” (p. 77)

Since this paper takes the related diversification strategy under the definition of ‘horizontal

integration’ the second hypothesis will be:

H2: Horizontal integration strategies outperform unrelated diversification strategies.

Based on the value-reducing and enhancing effects of global diversification, the prior

studies indicate conflicting evidence of geographic diversification on the firm’s value.

Researchers found that wide-ranging multinational operations were associated with higher

performance (Delios & Beamish, 1999; Hitt et al., 1997) and lower levels of risk. “However,

given that international operations encumber a firm because of the increased difficulty and

costs found in operating in foreign markets, it remained a question whether the higher

performance of multinational firms was attributable to a firm’s possession of superior

resources (i.e. proprietary assets5), or to other benefits of international operations.” (Delios &

Beamish, 1999, p. 723) The third hypothesis will test the positive aspect of geographic

diversification, taking into account that the geographically diversified firms have higher

values of performance measures such as operating income and ROA, compared to industrially

diversified firms ( Kim & Mathur, 2007).

H3: Geographic diversification has positive effects on firm’s performance.

5 Proprietary asset, usually, is any asset that is considered in the realm of intellectual property that should not be disclosed. These assets may include trade secrets and undisclosed inventions (VentureLine).


26

4. METHODOLOGY The study involves the analysis of 147 Danish firms between the years 2005-2009,

based on the 5-year average values. The recent empirical studies mostly focus on market share

prices and event studies in analyzing the diversification-performance linkage, whereas this

paper will focus on the operating performance perspective. Performance is measured as

operating revenue per employee (ORPE) and net income per employee (NIPE), which are

used as the dependent variables. The independent variables would be horizontal integration

(HI), vertical integration (VI), unrelated diversification (UR) and un-diversification (UD)

strategies which were explained with binary (dummy) variables that take on the values 1 and

0 depending on the type of strategy. Moreover, the analysis will examine the effects of global

diversification (COUNTRY) by focusing on the number of subsidiaries. Control variables

involve the firm specific characteristics such as: risk (RISK), size (SIZE), capital intensity

(CINT), market share (MARS), cost per employee (CPE) and the ratio of the cost of

employee to operating revenue (RATIO). Apart from these measures, this study has

conducted the Herfindahl index, entropy measure, concentration ratio and the relative measure

for the four largest firms in order to illustrate how concentrated and diversified the industries

are. These measures will not be included in the regression analysis, since the concentration

indices are calculated for all the years (2009-2005) rather than computing averages.

The analysis will begin by distinguishing each of the 5 industries and presenting their

descriptive statistics. This separation is crucial, since a computation of the summary statistics

of the whole sample would be misleading based on the differences among the industries.

In addition to these summary statistics, the study will present two regression models

with the inclusion and exclusion of the interaction effects (Bettis, 1981). The data for the

regression analysis will be conducted for only two industries separately, due to having

sufficient number of companies. These industries would be the manufacture of food and the

manufacture of machinery and equipment industries, with 54 and 48 companies respectively.

It will be designed to explore the performance differences between vertically integrated,

horizontal integrated, unrelated diversified and un-diversified firms. The models will be

estimated with the simple OLS regression, by conducting for ORPE and NIPE performance

measures separately. Below the models are briefly identified:

Model without the interaction effects: ORPE = β0 + β1 (SIZE) + β2 (RISK) + β3 (CINT) + β4 (MARS) + β5 (CPE) + β6 ( RATIO) + β7 (COUNTRY) + β8 (VI) + β9 (HI) + β10 (UR) + e


27

NIPE = β0 + β1 (SIZE) + β2 (RISK) + β3 (CINT) + β4 (MARS) + β5 (CPE) + β6 ( RATIO) + β7 (COUNTRY) + β8 (VI) + β9 (HI) + β10 (UR) + e In this model VI, HI and UR are dummy variables, in which the un-diversification

variable is excluded from the model. "This was done since if all the binary variables are

included, the normal regression equations are not independent and thus not have a unique

solution." (Bettis, 1981, p. 384) Therefore, β0 embraces the effects of the un-diversification

strategy (Bettis, 1981).

The second model includes the interactive terms in order to explore more the reasons

for differences in performance effects between different diversification strategies. In this type

of model the forward stepwise regression procedure was used (Bettis, 1981), in order to

“include every potentially useful predictor in the model and then delete those terms not

making significant partial contributions at some pre-assigned significance level.” (Agresti &

Finlay, 1997, p. 528) The forward selection begins with none of the variables and adds one

variable at a time to the model until it reaches a point where an inclusion of the remaining

variable does not make a significant contribution in predicting Y. In order to further modify

the forward selection, stepwise regression leaves the variables out from the model, in case

they lose their significance as other variables are added. Therefore, a variable previously

entered into the model at some point may be eliminated due to the overlap with other

variables that have entered at later stages (Agresti & Finlay, 1997). The interactive regression

model to be tested under this forward stepwise procedure is constructed as follows6:

Model with the interaction effects: ORPE = β0 + β1 (SIZE) + β2 (RISK) + β3 (CINT) + β4 (MARS) + β5 (CPE) + β6 (RATIO) + β7 (COUNTRY) + β8 (VI) + β9 (HI) + β10 (UR) + β11 (SIZE) (VI) + β12 (RISK) (VI) + β13 (CINT) (VI) + β14 (MARS) (VI) + β15 (CPE) (VI) + β16 (RATIO) (VI) + β17 (COUNTRY) (VI) + β18 (SIZE) (HI) + β19 (RISK) (HI) + β20 (CINT) (HI) + β21 (MARS) (HI) + β22 (CPE) (HI) + β23 (RATIO) (HI) + β24 (COUNTRY) (HI) + β25 (SIZE) (UR) + β26 (RISK) (UR) + β27 (CINT) (UR) + β28 (MARS) (UR) + β29 (CPE) (UR) + β30 (RATIO) (UR) + β31 (COUNTRY) (UR) + e

The same model will be conducted for net income per employee performance measure

(NIPE). Here, the inclusion of the interaction terms between diversification strategy and the

other variables were of major interest. These terms would strongly suggest reasons for

performance differences. For instance, the inclusion of β21 would suggest that one reason for

the high performance of horizontally integrated firms was the market share (Bettis, 1981) 6 Forward stepwise regression procedure is conducted using the Stata11 Statistics software program. The forward stepwise regression model is used by Bettis (1982) as well, in which the study involved a sample of 58 companies to identify the performance differences among related and unrelated firms.


28

5. DATA CONSTRUCTION 5.1. Sample Selection

This paper investigates the performance effects chosen among vertical, horizontal,

unrelated and undiversified strategies, using a sample that embraces the operating

performance measures’ of the Danish companies that are distinguished among 5 large

industries, through years 2009-2005. The decision to use a 5-year time period was based on

the motivation of having a long time period for the study, as well as preventing excessive

missing data by keeping the time frame limited (Capar, 2009).7 The company selection

process has been conducted in the Orbis Database, which covers 80 million companies around

the world, including 395,183 companies operating in Denmark (Appendix 1). Moreover, some

companies have been double checked in the WebDirect Database. The selection criterion was

restricted to Danish companies at the industry-level data that are operating in the manufacture

of food products (NACE Rev. 2, core code 10), manufacture of chemicals and chemical

products (NACE Rev. 2, core code 20), manufacture of basic pharmaceutical products and

pharmaceutical preparations (NACE Rev. 2, core code 21), manufacture of machinery and

equipment (NACE Rev. 2, core code 28) and manufacture of furniture (NACE Rev. 2, core

code 31) industries8. The service firms are excluded in order to diminish the confusing effects

of the differences between manufacturing and service firms (Ravichandran et al. 2009).

“Moreover, there are significant differences between manufacturing and service firms in their

disaggregation of financial data by business activities.” (Ravichandran et al. 2009, p. 218)

Therefore only manufacturing firms are to be chosen from the 2-digit NACE industry

classification, and meeting the following criteria: (1) years with available accounts: 2009-

2005. (2) Number of employees having a minimum value of 10, for all the years. (3)

Operating revenue (turnover) with a known value for all the years.

According to the sampling criteria defined above, an initial sample of 158 companies

was obtained. Out of these firms, 11 of them are eliminated due to being holding companies.

These holding companies generally had more than one primary NACE code, which could not

be distinguished among the industries the company is operating in. Based on the industries; 1

firm from the manufacture of food products, 3 firms from the manufacture of chemical, 3

firms from the manufacture of pharmaceuticals, 1 firm from the manufacture of furniture, and

7 The diversification strategies (VI, HI, Unrelated and Undiversified) are stable and the same over the defined time. 8 Industries are chosen based on the research of the most crucial industries in Denmark.


29

finally 3 firms from the manufacture of machinery industries are eliminated. The final sample,

which is a balanced panel, is composed of 147 companies with 1911 firm average year

observations (Appendix 2).

Table 1: Final number of companies by industry

Industries Number of Companies Pharmaceutical Industry 11 Food Industry 54 Chemical Industry 19 Furniture Industry 15 Machinery Industry

48

Total 147 Although the analysis will be based upon 5-year average values of the variables, the

sample is under the category of a panel data, since it contains time series observations of a

number of individuals (Hsiao, 2005). This type is combining both the time-series and cross-

sectional data analysis and “looks at multiple subjects and how they change over the course of

time.”(Wikipedia) Several advantages that the panel data has over cross-sectional and time-

series data could be classified as: (1) More correct assumptions of model parameters. (2)

Greater capacity for confining the complexity of human behavior compared to a single cross-

section or time-series data. (3) Simplifying computation and statistical analysis (Hsiao, 2005).

Besides using the Orbis Database in collecting the data, the Input-Output tables are

gathered in order to analyze the presence of vertical integration. “With the IO data, we can

capture the vertical relationship between a pair of merging firms from the dollar amount of

input transfer between the industries in which the merging firms operate.” (Fan & Goyal,

2006, p. 878) If a company uses the other’s products or services as input or vice versa, the

two industries are categorized to be vertically related. The IO tables are obtained from

statbank.dk, where detailed statistical information on the Danish society exists (Appendix 3).

The majority of the studies that have been conducted in the field of diversification are

classifying the firm’s integration strategies with the use of the SIC codes (Santalo & Becerra,

2008; Miller, 2006; Ravichandran et al. 2009). Primarily in this study, the firms’ integration

strategies are classified according to the first 2-digit NACE Rev. 2 codes. Different from the

world level SIC codes, NACE codes are on the EU level. In addition, “NACE is derived from

ISIC, in the sense that it is more detailed than ISIC.” (NACE Rev. 2 Guide, p. 14) They have

exactly the same items at the highest levels, where NACE is more detailed at lower levels.

Based on the primary and secondary 2-digit NACE codes, the procedure used to categorize

the companies according to their choice of integration strategy is as follows:


30

• Firms with one NACE code (primary only): Undiversified firm9

• Firms with the same primary and secondary 2-digit NACE codes: Horizontally

integrated

• Firms with different primary and secondary 2-digit NACE codes:

o Checked the IO table to trace vertical integration, if the supplying industry

exceeds the 1% average threshold10, the company is vertically integrated.

o If the supplying industry falls below the 1% threshold, the company is

unrelated diversified.

• Firms with more than one secondary 2-digit NACE codes are classified based on the

importance level of the NACE codes. The first code to be reported has been regarded

as the most crucial. (Appendix 4)

Table 2 illustrates the number of companies that fall under each type of integration strategy

out of the 147 companies.

Table 2: Number of companies based on integration strategies

Strategies Number of Companies Vertical Integration 27 Horizontal Integration 35 Unrelated Diversification 31 Undiversified

54

Total 147

5.2.Variables Measurement

5.2.1. Performance Measures (Dependent Variables)

Prior studies have put a large emphasis on return on assets (ROA) and return on sales

(ROS) in taking these variables as performance measures (Kahloul & Hallara, 2010; Capar,

2009; Ravichandran, 2009; Bettis, 1981). This study will analyze the management

effectiveness of the Danish companies with operating revenue per employee and net income

per employee measures (Appendix 5).11 The operating revenue per employee simply measures

the amount of the currency sales, or revenue, generated per employee and high levels of this

9 Double checked the company products and activities from their websites; based on the activities 6 of the companies have been changed from undiversified to either HI or unrelated. 10 1% index has been computed looking at the first 10-15 supplying industries’ average percentages through years 2005-2007. 11 In Denmark, the companies’ assets may be misrepresentative. This conclusion is reached by examining the ROA ratios of Novo Nordisk A/S (See Appendix 5).

http://www.investopedia.com/terms/r/revenue.asp


31

indicator is preferable. However compared to a high-tech industry, the labor-intensive

industries may be less productive and generate low levels of the indicator (Investopedia).

Operating revenue per employee (ORPE) = Operating Revenue (Turnover) / Number of employees The other performance measure, net income per employee is taken as an indicator of

management efficiency. Net income per employee measures the ratio of operating income to

the number of employees that is required to produce that level of income.

Net income per employee (NIPE) = Net Income /Number of employees Therefore, net income per employee determines the management's ability to utilize

their employee resources effectively in order to generate profits for the company.

Comparisons of income per employee should only be made between companies in similar

industries. When comparing two companies, the company with a higher value for income per

employee is to be more efficient (Money-Zine)

5.2.2. Independent Variables

The integration strategies of vertical, horizontal, unrelated and undiversified strategies

are defined as mentioned above. These variables will be binary (dummy) variables that take

on the values 1 and 0 depending on the integration strategy of the firm (Bettis, 1981). A value

of 1 will represent the existence of the strategy, whereas a value of 0 indicates that the firm

has not undertaken that particular integration strategy. For simplicity, it is crucial to note that

the choices of strategies are assumed to be mutually exclusive, in which a company cannot

undertake more than one integration strategy.12 In the real world, it is most likely to have a

company with more than one integration strategy; however this assumption will help to assign

the effects of a specific integration choice on performance measures more explicitly.

Apart from the strategies defined above, geographic diversification has been taken into

consideration in the past research. Several measures for this type of diversification that have

been used would be, (1) the measure of international sales as a percentage of total sales, (2)

the number of overseas subsidiaries, (3) the Herfindahl index, (4) the entropy measure, (5)

and the number of countries in which a firm has overseas subsidiaries (Ravichandran et al.

2009). Ravichandran et al. (2009) underlines that, each measure has its own advantages and

12 One particular strategy among the four choices has to be identified for each firm.


32

limitations. This study will use the number of countries in which a firm has foreign

subsidiaries, in order to reflect the dispersal of the company’s functions across countries.

Moreover, this paper will present some measures of the concentration indices, in order

to give an understanding on how concentrated and diversified the industries are. The measure

of the diversification of the firm has been valued from two continuous measures which are the

Herfindahl index and the entropy measure (Kahloul & Hallara, 2010). The Herfindahl index is

a measure of market concentration, where “the ratio of the firm’s sales within the firm’s

primary industry to the firm’s total sales” (Jacquemin & Berry, 1979, p. 359) is computed.

In the formula, “n is the number of firm’s activities and Pi is the relative weight of each

activity evaluated as the proportion of the sale xi of the activity i of a firm.” (Kahloul &

Hallara, 2010, p. 152) A rise in the Herfindahl index usually depicts a decrease in competition

and an increase of market power, whereas reductions indicate the opposite. The higher the

value of the index, the less likely a given industry will reveal competitive behavior

(Lipczynski & Wilson, 2001). Moreover, the Horizontal Merger Guidelines of U.S. Federal

Trade Commission has presented ranges in specifying three types of concentration:

• Un-concentrated Markets: HHI below 0.15

• Moderately Concentrated Markets: HHI between 0.15 and 0.25

• Highly Concentrated Markets: HHI above 0.25 (part 5.3)

On the other side, the entropy measure is the inverse of the Herfindahl index that weighs each

market share (Pi) by the logarithm of Pi.

It is a measure that enumerates the degree of uncertainty in a given industry, and the lower

value of the index would expose greater certainty of the established firms’ future relationships

with the buyers in the market. “The entropy measure is also more sensitive than the

Herfindahl index to very small firms.” (Jacquemin & Berry, 1979, p. 360) Since E is an

inverse concentration measure, the value is small for highly concentrated industries, whereas

large for a low concentrated industry (Lipczynski et al. 2005). Moreover, by dividing the

entropy measure by the number of companies, relative measure (RE) could be obtained,

which provides convenience in making comparisons among industries. “The minimum


33

possible value is RE=0 for a monopoly, and the maximum possible value is RE=1 for an

industry comprising N equal-sized firms.” (Lipczynski et al. 2005, p. 203)

In addition to these indices, concentration ratio (CR4) for the four largest companies

can be calculated, in order to illustrate the scope of market control of the largest firms in the

industry and to present the degree to which an industry is oligopolistic (Wikipedia). However,

“the concentration ratio measure suffers from the problem that it only focuses on the top firms

in the industry and takes no account of the distribution of remaining firms.” (Lipczynski &

Wilson, 2001, p. 109)

CRm= Σmi=1 si

Where, m is the number of firms taken into account (which is 4 in this study) and si is the

market share of the firm i.

Based on these measurements, the market shares in this study are computed by

summing the operating revenues of all the firms in one particular industry for that year, and

dividing each company’s individual operating revenues to this total industry turnover. This

industry turnover is taken to be a representative value for the whole industry, since the Orbis

Database could not identify applicable operating revenue values for all the companies. In

addition; the measures that are obtained from Orbis were not adequate enough to distinguish

the firm’s activities within its operating sales. Moreover, the analysis for the concentration

indices is conducted for 4 years, due to the missing values of 2005 for the pharmaceutical

company Novo Nordisk A/S.13

5.2.3. Control Variables

The study involves several control variables in order to determine the effect of

integration strategies on the firm performance by eliminating the other affects on firm

variables. Based on the theories developed to enlighten the integration strategies, empirical

studies have commonly used the factors of size, risk, and capital intensity as control variables

(Bettis, 1981; Ravichandran et al. 2009) In addition to these variables average market share,

average cost per employee and the ratio of average cost per employee to average operating

revenue per employee will be included in the analysis14.

13 Novo Nordisk A/S did not have an applicable operating revenue value for the year 2005. In order to have an accurate representation of the indices the analysis of concentration indices is limited through years 2009-2006. The analysis of average market shares is to be taken for 5 years, besides Novo Nordisk A/S. 14 Other common control variables such as R&D expenditure and Added Value were not applicable in Orbis Database.


34

SIZE– Kumar et al. (1999) has conducted a study of cross-country analysis in which

they have found “that institutional factors such as the efficiency of the judicial system and the

development of financial markets as well as technological factors such as capital intensity and

market size seem to influence the size of firms.” (p. 30) The managerial literature has covered

a number of variables to measure firm size including number of employees, average assets,

and average sales (Leiblein & Miller, 2003). The firm size in this study is measured as the

average natural logarithm of total employees over the past 5-year period, 2009-2005.

RISK – Bettis (1981) points out to the limitation of the empirical work conducted on

the relationship between profits and risk. However, the importance of risk as an economic

variable has been accepted for many years. The term risk is commonly used to define some

degree of hazard, which could be financial as bankruptcy or solvency. “Such risk can result

from a variety of sources such as short-term fluctuations in profits, changes in consumer

tastes, changes in technology, changes in government policy and strategic moves of

competitors.” (Bettis, 1981, p. 383) For instance, in the studies of Fisher & Hall (1969) that

included 11 different industries, observed a positive relationship among risk and profit within

the industries. Moreover Bettis & Hall (1982) observed in a study of 80 large diversified firms

through years 1973-1977 that unrelated diversified firms illustrated a positive relationship

between return on assets and the standard deviation of the return on assets, whereas no

relationship or negative one was detected for related diversified companies. Although most

studies of risk has been conducted at the degree of the securities markets, this paper will

include the variable of risk as the measure of standard deviation of return on assets over the

average period of 2009 to 2005.

CINT –A company would be capital intensive if a business process demands large

amounts of money and other financial resources to produce a good or service. Capital

intensity will be based on the ratio of the capital required to the number of labor that is

required. Oil production and refining, telecommunications and transports such as railways and

airlines industries could be given as examples of having high capital intensity. “Companies in

capital-intensive industries are thus often marked by high levels of depreciation and fixed

assets on the balance sheet.” (Investopedia) Therefore, it could be underlined that the capital

intensity of industries varies widely and some industries could be more capital intensive based

on the nature of the technology (Bettis, 1981). Moreover Porter (1976) has indicated that

capital intensity may act as a barrier to exit if taken as an industry specific asset. “In general,

capital intensity imposes a greater degree of risk because assets are frozen in long-lived forms

that may not be easy to sell.” (Bettis, 1981, p. 382) In this study, capital intensity is measured


35

by taking the ratio of average fixed assets to the average number of employees over the years

2009-2005.

MARS –The companies’ market shares are computed by dividing the company’s sales

in a particular period by the total sales of the industry at that same period. This variable will

be giving a general idea of the size of a company regarding its markets and competitors. The

rise and fall of the market share would be an indicator of the relative competitiveness of the

company's products or services. Therefore, a company that is increasing its market share will

observe a growth in its revenues, which would be faster compared to its competitors.

Economies of scale and improvement in profitability could be achieved based on the increases

of market share (Investopedia). Based on this phenomenon, the average value of the 5-year

market shares for each firm is computed, and taken as a control variable in analyzing the

effects on performance.

CPE –Average cost per employee is used in this study, in order to take the effect of

how much each employee would cost based on the total costs of the firm. The total costs

would be the sum of fixed costs, variable costs and semi-variable costs (InvestorWords) The

Orbis database had this measure calculated.

RATIO – The average of cost of employee to operating revenue ratio will represent if

the costs of the employees are exceeding the company’s operating revenue. In other words, it

is to observe how many times the costs are exceeding the revenues. This control variable will

help to examine the effect of this ratio on the firm’s performance measurement.

Based on these definitions, Table 3 summarizes the calculations of the variables

presented above.

Table 3: Variable Definitions

Variable Definitions Formulas Average Operating Revenue per Employee Avg. Operating Revenue / Avg. Number of

Employees Average Net Income per Employee Avg. Net Income / Avg. Number of Employees Size SIZE=1/ln(Number of Employees) Risk RISK= Standard deviation of ROA for 2009-

2005 Capital Intensity CINT=Avg. Fixed Assets / Avg. Number of

Employees Average Market Shares MARS=Total Market Share (for 5 years) /5 Average Cost per Employee CPE=Total Cost per Employee (for 5 years) /5 Ratio of Cost & Revenue RATIO=(Average) Cost of Employee /

Operating Revenue


36

Although the study’s aim is to differentiate the values among the industries, it is worth

to summarize the whole data as illustrated in Table 4. The sample of the 147 firms has

average operating revenue per employee (ORPE) of 2,622 and a net income per employee

(NIPE) of 116. Due to the differences in industries; the performance measures, capital

intensity, and market share variables have high volatility, standard deviation. This volatility

could also be observed by the huge differences of the minimum and maximum values

presented. Therefore, the separate analysis of the industries aims to reduce this volatility and

attain more accurate representations of how the integration choices affect firm performance.

Table 4: Summary statistics

Variable Observations Mean Std. Dev. Min. Max. ORPE 147 2,621.8 3,753.5 403.3 40,836 NIPE 147 116.2 299.9 -514.9 2,114.1 RISK 147 7.80 7.06 0.18 43.37 SIZE 147 0.21 0.07 0.10 0.40 CINT 147 951.5 1,713 14.5 18,134 MARS 147 0.03 0.09 0.0001 .65 CPE 147 406.28 101.86 110.56 919.3 RATIO 147 24.41 13.57 1.27 93.65 COUNTRY 147 4.36 9.20 0 64

5.3.Limitations

Due to the scope of this study, limited number of industries and firms may not

represent the whole Danish economy. The number of industries is to be taken as the 5 biggest

industries, based on the highest number of firms involved in those industries. Restricting the

number of industries leads to the restriction of the sample size as well. A longer time period

would be recommended to more effectively capture the effects of the sample. The data had to

be restricted to include the firms with available sales figures and the other variable

measurements, therefore the descriptive statistics had to be computed with small number of

firms within each industry. Moreover, only manufacturing firms are taken into consideration,

which may confine the generalizability of the findings. In addition, a crucial limitation would

be the lack of identifying the relative shares of the various activities within the firm-level.


37

6. GENERAL DESCRIPTIVE ANALYSIS OF EACH INDUSTRY

As mentioned above, it is crucial to differentiate the industries in order to investigate

how the effect of the integration strategies on performance vary based on the industry. Santalo

& Becerra (2008) are avoiding taking the effect of diversification on performance as

homogenous across industries, but rather illustrating that “diversified firms observe a

diversification discount if and only if they compete in industries with a large number of

single-segment companies, or enjoy a premium in those industries in which only a few non-

diversified firms compete.” (p. 851) Moreover, Montgomery & Christensen (1981) have

examined significant performance differences among Rumelt’s categories of diversification

and the market structure variables (market share, market concentration, market growth and

firm size). The market structure-performance linkage has suggested that “firms located in

markets which constrain their growth or profitability is the most likely candidates for

diversification.” (p. 338) Therefore, firms in low opportunity markets have the tendency to

pursue unrelated diversification.

This section will illustrate the summary statistics output15 for the industries;

manufacture of basic pharmaceutical products and pharmaceutical preparations (NACE 21),

manufacture of food products (NACE 10), manufacture of chemicals and chemical products

(NACE 20), manufacture of furniture (NACE 31), and finally manufacture of machinery and

equipment respectively (NACE 28). Out of these 5 industries, the manufacture of food

products and the manufacture of machinery and equipment industries will be subject to be

tested under the OLS regression. The remaining industries will be out of the analysis due to

having insufficient number of companies. Moreover, it is crucial to note that the data consists

of firms only having applicable values for the variables, therefore taken to be as the

representatives of the whole industry.

6.1. Manufacture of Basic Pharmaceutical Products and Pharmaceutical Preparations

The data for the pharmaceutical industry that has been obtained from the Orbis

database comprised of an initial sample of 14 companies, which had applicable values for the

variables. 3 of the companies (Origio A/S, Exiqon A/S & Lifecycle Pharma A/S) have been

eliminated due to being holding companies, leaving a sample of 11 companies.

Based on the comparisons of the primary and secondary NACE codes of the firms, the

mutually exclusive integration strategies are differentiated. Out of these 11 companies, 4 are

15 Obtained from the statistics program Stata11.


38

vertically integrated, 1 is horizontally integrated, 3 are unrelated diversified and 3 of them are

undiversified (Appendix 6, T. 11) 16.


Strategy VI HI UnRe. UnDiv. Total Num. of Firms 4 1 3 3 11 Although the sample embraces only one horizontally integrated company, it is worth

illustrating for each integration strategy the averages of the profitability measures, market

shares and the numbers of countries the companies are operating in.

Table 6: General analysis based on integration strategies

Analysis VI Std.

Dev. HI Std.

Dev. UnRe. Std.

Dev. UnDiv. Std.

Dev. Avg. ORPE17

1,482.9 573.33 2,247.5 - 2,754 1,262.69 1,662.7 972.97

Avg. NIPE 669 1,100.8 1,053.2 - 450.92 481.66 125.34 442.18 Avg. MARS 0.08 0.17 0.02 - 0.22 0.24 0.04 0.06 Avg. COUNTRY

16 19.24 4 - 31 31.66 9 15.01

Since the VI, unrelated and undiversified strategies have more or less the same number

of firms; among them the unrelated integration strategy has the highest average operating

revenue per employee measure (2,754), whereas the vertically integrated firms have the

lowest (1482.9), as shown in Table 6. Moreover, the companies with an unrelated

diversification strategy are enjoying larger market shares on the average (22%) and they are

more dispersed in foreign countries (31). As it will be mentioned below, this could be due to

the low competition within the industry, where the companies observe an advantage in

seeking other profitable industries in which to participate. These seek will in return permit a

wider range of areas to work in for the companies (Bettis & Hall, 1982). From the net income

per employee (NIPE) point of view, it is observed that vertically integrated companies tend to

have the highest value on the average (without taking into account the HI strategy). However

it is important to note that, there is high volatility in the value of NIPE for all the integration

strategies, where some companies have reported negative values of NIPE whereas others

reported positive.

In order to have an understanding of the competition within the industry, and the big

players’ market shares, it is crucial to examine the concentration indices of the industry. The 16 T.11 stands for Table 11 under Appendix 6. 17 The variables are defined in detail under subsection 5.2 Variables Measurement.


39

indices have been conducted for a 4 year period (2009-2006) instead of a 5 year for all the

industries, due to the missing values for the year 2005 for Novo Nordisk /AS.

Table 7: Concentration indices

Concentration Indices

2009 2008 2007 2006

Entropy Measure 1.505 1.518 1.526 1.406 Herfindahl Index 0.287 0.290 0.294 0.333 Relative Measure 0.137 0.138 0.139 0.128 CR4 0.916 0.905 0.906 0.939 According to Table 7, it is observed that the Herfindahl index has been slightly

decreasing through the years, indicating an increase of competition and a decrease in market

power. The average HHI would be 0.301 which is above the 0.25 threshold (Horizontal

Merger Guidelines) therefore highlighting high concentration, meaning that this industry is

not competitive and has dominant players. Moreover, the concentration ratio (CR4), which is

the sum of the 4 biggest players in the industry, is illustrating a slight decrease in their market

shares due to this increasing competition. However, the overall level of competition is low in

the industry and CR4 is supporting this with the high level of market shares. The entropy

measure is varying oppositely to the Herfindahl index, since the sum of the products of

market shares to its natural logarithm are taken. The relative measure is the value of the

entropy measure divided by the number of firms, in order to be able to make comparisons

among the industries18. Moreover, the descriptive statistics below indicates a high standard

deviation for the average market shares (MARS), which is greater than the mean. This is

specifying a wide range of market shares, and if examined individually it is observed that for

the year 2009 Novo Nordisk A/S has 22.97% more market share than H. Lundbeck A/S, the

company with the second highest market share.

Table 8: Descriptive statistics of pharmaceutical products and pharmaceutical preparations industry

18 RE will be analyzed further when comparing the industries.

Variable Obs. Mean Std. Dev. Min. Max. ORPE 11 1,948.1 960.02 636.68 4,110.3 NIPE 11 496.13 730.66 -514.92 2114.07 RISK 11 9.60 8.62 0.58 29.47 SIZE 11 0.17 0.07 0.11 0.33 CINT 11 3,465.02 5,158.11 577.56 18,133.7 MARS 11 0.10 0.15 0.0005 0.48 CPE 11 562.42 129.47 447.33 919.30 RATIO 11 38.49 21.95 15.37 93.65 COUNTRY 11 16.91 21.23 0 64


40

Table 8 contains the summary statistics for the manufacture of pharmaceutical

products industry by taking the average values for the period 2009-2005 (Appendix 6, Output

1). The firms have average operating revenue per employee of 1,948 and an average net

income per employee of 496. This difference could be due to the two companies (Bavarian

Nordic A/S and Mekos Laboratories ApS) that have reported negative average values of net

income through the 5 year period. The effect of these negative values could be observed by

the high standard deviation which is 730.66. Apart from this, high volatility could be detected

for the average capital intensity, market share and the number of countries, which could be

due to the small number of sample size. Moreover, examining the values of kurtosis and

skewness for the variables would indicate whether the data is peaked or flat respectively

compared to the normal distribution and if the data is lack of symmetry (Engineering

Statistics Handbook). “A distribution that is not symmetric, but rather has most of its values

either to the right or to the left of the mode, is said to be skewed.” (Harnett & Soni, 1991, p.

34) The value of kurtosis being near the value of 3 and 0 for skewness would indicate a

normal distribution. Based on these definitions, the values for capital intensity (CINT),

market share (MARS), cost per employee (CPE) and the ratio of cost of employee to

operating revenue (RATIO) measures have moderate and positive kurtosis and skewness

(Appendix 6, Output 2). For these data sets, there is a peaked and right skewed distribution

meaning that few companies exist with a value greater than the mean of the measurement

(Appendix 6, Output 4).

Table 9: Correlations The correlations presented at Table 9,

are indicating a positive correlation for

operating revenue per employee with the

variables net income per employee, size,

market share, cost per employee, horizontal

integration, unrelated diversified and the

number of countries. Moreover, the net

income per employee measure is positively

correlated with capital intensity, market share,

vertical integration, horizontal integration and

the number of countries. Therefore, for both of the performance measures the risk, ratio (cost

of employee to operating revenue) and un-diversification strategies are not favorable in which

negative correlation exists. Among the integration strategies, unrelated diversification strategy

Correlation ORPE NIPE ORPE 1.00 NIPE 0.17 1.00 RISK -0.30 -0.13 SIZE 0.04 -0.43 CINT -0.05 0.78 MARS 0.16 0.20 CPE 0.38 -0.04 RATIO -0.74 -0.45 VI -0.38 0.19 HI 0.10 0.25 UR 0.54 -0.04 UD -0.19 -0.33 COUNTRY 0.10 0.20


41

has the highest correlation with ORPE (0.539) which supports the high average value of the

companies under this category. In addition, this industry favors geographic diversification by

indicating a positive correlation with the performance measures and the unrelated integration

strategy (Appendix 6, Output 3).

6.2.Manufacture of Food Products

Following the manufacture of pharmaceuticals industry, data for the food industry has

been obtained, with an initial sample of 55 companies. East Asiatic Co. LTD A/S has been

eliminated due to being a holding company, leaving a sample of 54 companies.

Out of the 54 firms, it is identified that 6 of them are vertically integrated, 16 are

horizontally integrated, 9 are unrelated diversified and 23 companies are undiversified. By

looking at these numbers, one could say that this industry dominates the un-diversification

strategy (Appendix 7, T. 12).


Strategy VI HI UnRe. UnDiv. Total Num. of Firms 6 16 9 23 54 According to the analysis presented below in Table 11, the profitability measure of

operating revenue per employee (ORPE) is the highest for horizontally integrated companies

(5,016) followed by vertical integration (4,182.7), unrelated diversified (3,540) and

undiversified strategies (2,724). Although the unrelated companies are not observed to be the

lowest performing, the horizontally integrated companies are outperforming the unrelated

diversified companies based on the average ORPE performance measure, as this

outperformance has been supported by previous studies (Bettis, 1981; Miller, 2006 & Rumelt,

1974). From the NIPE point of view, the unrelated diversified companies tend to have the

highest on average, however representing high volatility due to the number of employees that

each company embraces and positive or negative values of net income announced.


Analysis VI Std. Dev.

HI Std. Dev.

UnRe. Std. Dev.

UnDiv. Std. Dev.

Avg. ORPE 4,182.7 3,117.4 5,016 9,679.9 3,540 3,552.30 2,724 2,096.7 Avg. NIPE 22.63 159.93 78.82 121.95 194.05 329.07 36.55 82.23 Avg.MARS 0.01 0.007 0.04 0.12 0.03 0.06 0.005 0.005 Avg.COUNTRY 3 3.50 2 3.24 7 12.83 2 4.58


42

By having a high number of undiversified companies, which represents 42% of the whole

industry, it is crucial to examine the level of concentration within the industry. Table 12

illustrates the indices computed through the years 2009 to 2006.


Concentration Indices 2009 2008 2007 2006

Entropy Measure 2.120 2.153 2.125 2.017 Herfindahl Index 0.252 0.243 0.248 0.278 Relative Measure 0.040 0.041 0.040 0.038 CR4 0.659 0.684 0.700 0.731 Until the year 2009, a slight decrease in Herfindahl index is observed, indicating an

increase in competition. According to the Horizontal Merger Guidelines, in the years 2006

and 2009 the industry was highly concentrated (0.278 and 0.252 respectively) since the HH

index is exceeding the 0.25 threshold. Between the years 2008 and 2007, moderate

concentration existed, due to having a value in between 0.15 to 0.25 thresholds. Moreover a

slight decrease of the concentration ratio of the 4 biggest players is observed. The increase in

competition could be due effect of the dominant undiversified companies, since they are

aiming to protect and increase the market shares within the industry, without integrating.

Moreover, as Jacquemin & Berry (1979) has highlighted, the entropy measure reveals the

degree of uncertainty in a given industry and the lower values would indicate greater certainty

of the firms’ relationships with the buyers in the market. Therefore, compared to the

pharmaceutical industry, the food industry reveals greater uncertainty.

The summary statistics for the manufacture of food industry, presented in Table 13,

depicts average operating revenue per employee of 3701.21 and a net income per employee

measure of 73.78. Both of these measures present high volatility (5668.6, and 170.69

respectively). The other variables having high variability are the risk (6.59), market share

(0.06), and the number of countries the firm is operating in (6.35). The variability in market

share could be due to the low competition with dominant players in the industry, in which the

Leverandorselskabet Danish Crown Amba Company has a market share of 47.54% (more

than 30% of the second company with the highest market share). The volatility of the number

of countries is also understandable, since there are 9 companies that are unrelated diversified

in an industry where 23 undiversified firms are operating. Moreover, apart from the variables

size and cost per employee, the remaining values are positively skewed with high kurtosis

(Appendix 7, Output 5-6).


43

Table 13: Descriptive statistics of food industry

Besides the analysis presented above, the coefficient correlations are demonstrated in

Table 14. The results show that risk, market share, cost of employee to operating revenue

ratio, unrelated integration, un-diversification strategy, and the number of countries are

negatively correlated with the operating revenue per employee measure. The positive

correlation of horizontal and vertical integration strategies are reasonable, taking into account

the highest values of ORPE to be presented above. The other performance measure, net

income per employee, is negatively correlated with market share, vertical integration, un-

diversification strategy and the number of countries. This would be explained as; any rise in

of these coefficients or attempt to undertake the integration strategies would result in a

decrease in the performance measures.

Moreover a positive correlation exists with

risk, size, capital intensity, cost per employee,

and cost of employee to operating revenue

ratio, horizontal and unrelated integration;

meaning that a rise or decrease in one of the

variables will affect the performance measure

of NIPE in the same direction. The food

industry is not favoring geographic

diversification, due to the negative correlation

Table 14: Correlations of both performance measures with the number of countries


Variable Obs. Mean Std. Dev. Min. Max. ORPE 54 3,701.2 5,668.6 783.47 40,836 NIPE 54 73.78 170.69 -385.58 820.71 RISK 54 5.99 6.60 0.18 33.81 SIZE 54 0.21 0.06 0.10 0.37 CINT 54 1,055.7 939.09 79.44 4,692.9 MARS 54 0.02 0.07 0.0002 0.48 CPE 54 392.79 95.34 188.35 707.62 RATIO 54 17.86 10.50 1.27 58.58 COUNTRY 54 3.28 6.36 0 40

Correlation ORPE NIPE ORPE 1.00 NIPE 0.25 1.00 RISK -0.17 0.22 SIZE 0.47 0.41 CINT 0.63 0.21 MARS -0.05 -0.03 CPE 0.36 0.47 RATIO -0.45 0.18 VI 0.03 -0.11 HI 0.15 0.02 UR -0.01 0.32 UD -0.14 -0.19 COUNTRY -0.08 -0.09


44

6.3.Manufacture of Chemicals and Chemical Products

The manufacture of chemicals and chemical products industry comprised of 22

companies initially, in which 3 of them (Auriga Industries A/S, Flugger A/S and SP Group

A/S) have been eliminated due to being holding companies, leaving a sample of 19 firms.

These 19 companies are distinguished in Table 15 as 2 vertically integrated, 6

horizontally integrated, 5 unrelated diversified and 6 of them are undiversified. This

distribution does not highlight a specific dominant strategy that is undertaken within this

industry (Appendix 8, T. 13).


Strategy VI HI UnRe. UnDiv. Total Num. of Firms 2 6 5 6 19 According to the integration strategies, it is illustrated in Table 16 that horizontally

integrated companies have been outperforming the remaining strategies with an average value

of 4132.93 operating revenue per employee and an average market share of 13%. It is

followed by undiversified companies (3046.18), vertically integrated firms (2432.6) and the

lowest being the unrelated diversified companies (2174.22). These values are supporting the

findings of Rumelt (1982) where on the average related diversification strategies

outperformed the other integration strategies, whereas the unrelated business strategy was the

lowest performing. From the perspective of net operating income per employee, high

volatility exists for the companies that have undertaken horizontal integration, unrelated

diversification and un-diversification strategies. Moreover, the unrelated diversified

companies have the highest amount of countries that are operating in.



HI Std. Dev.

UnRe. Std.Dev. UnDiv. Std. Dev.

Avg. ORPE 2,432 317.62 4,132.9 2,636.8 2,174.2 822.27 3,046.2 3,254.5 Avg. NIPE 297.63 188.76 222.02 290.20 87.89 138.32 3.47 87.65 Avg. MARS 0.005 0.001 0.13 0.26 0.02 0.03 0.005 0.005 Avg. COUNTRY

2 0.71 6 8.94 11 21.74 4 8.09

The concentration indices presented below in Table 17 are indicating high values of

concentration and low competition, since the values for the Herfindahl index is above the 0.25

threshold. Moreover through the years 2009 to 2006 first an increase and then a fall of the HH


45

index is observed, specifying an increase in competition. Due to this increase, the first 4

biggest players in the market have experienced a slight fall in their market shares. In addition,

the entropy measure reveals greater certainty about the future relationships of the companies

due to its lower value compared to the previous industries.



2009 2008 2007 2006

Entropy Measure 1.222 1.085 1.066 1.098 Herfindahl Index 0.368 0.452 0.479 0.451 Relative Measure 0.064 0.057 0.056 0.058 CR4 0.792 0.815 0.823 0.801 The summary statistics for the manufacture of chemicals industry is presented in Table

18, highlighting average operating revenue per employee to be 3095.31 and net income per

employee 125.67. The values with high volatilities would be the net income per employee,

market share and the number of country measures. The effects of these high deviations are

observed from the values of skewness and kurtosis as well. For instance, the values for market

share are demonstrated as 3.89 and 16.43 respectively, which are beyond the values of 0 and 3

for a normal distribution. Since there is low competition with dominant players in the market,

it is reasonable to observe a massive difference between minimum and maximum values of

market shares (0.0001 and 0.65 respectively) (Appendix 8, Output 8-9).

Table 18: Descriptive statistics of chemicals industry

Variable Obs. Mean Std. Dev. Min. Max. ORPE 19 3,095.3 2,381.9 601.7 9,517.2 NIPE 19 125.67 208.60 -144.19 708.35 RISK 19 7.24 6.93 1.12 24.24 SIZE 19 0.21 0.07 0.11 0.40 CINT 19 952.8 908.43 14.52 4,292.2 MARS 19 0.05 0.15 0.0001 0.65 CPE 19 442.5 93.13 293.88 640.90 RATIO 19 20.47 11.89 5.47 51.26 COUNTRY 19 5.95 12.55 0 50

Correlation ORPE NIPE ORPE 1.00 NIPE 0.45 1.00 RISK -0.09 -0.06 SIZE 0.13 -0.02 CINT 0.55 0.46 MARS 0.53 0.32 CPE 0.47 0.43 RATIO -0.69 -0.35


46

In order to give a deeper understanding

of the values presented above, Table 19

illustrates the correlation coefficients for the

chemicals industry. Based on the table, the

Table 19: Correlations operating revenue per employee performance measure is negatively

correlated with risk, cost of employee to operating revenue ratio, vertical integration,

unrelated diversification, un-diversification strategies and the number of countries. A rise in

one of these values will lead a fall in the value of ORPE. On the other side, net income per

employee performance measure is negatively correlated with risk, size, ratio, unrelated

diversification, un-diversification strategies and the number of countries. Therefore, it could

be said that either the performance measures or the strategies/control variables are not

favoring each other, in terms of being risky, unrelated, undiversified, geographically

diversified, and having high cost of employee to operating revenue ratio (Appendix 8, Output

10).

6.4.Manufacture of Furniture

The third industry, the manufacture of furniture, involved 16 companies with one

holding company (Boconcept Holding A/S). After the elimination the sample is left with 15

companies, with 4 vertically integrated, 2 horizontally integrated 2 unrelated diversified and 7

undiversified firms. This sample is dominated by undiversified companies by 47% of the

industry (Appendix 9, T. 14).


Strategy VI HI UnRe. UnDiv. Total Num. of Firms 4 2 2 7 15 As it has been generally analyzed, Table 21 indicates the highest value of the

operating revenue per employee as 1603.82 and the market share as 19%, being under the

horizontal integration strategy. This is followed by the unrelated diversification, vertical

integration and un-diversification strategies. On the other side, the average net operating

income values are highest for the undiversified companies, followed by horizontal integration,

vertical integration and a negative value for unrelated diversification. It is again crucial to

note that the sample size is small in order to be able to demonstrate a very accurate

representation for the whole industry. However, these values can indicate that within this

industry, the average operating revenue per employee figures are not volatile among the

strategies to be chosen. The differentiation is more explicit with the net income per employee

VI -0.10 0.29 HI 0.30 0.32 UR -0.24 -0.11 UD -0.01 -0.41 COUNTRY -0.11 -0.07


47

performance measurement, where the undiversified companies are benefiting most and the

unrelated diversifiers the least.



HI Std. Dev.

UnRe. Std. Dev.

UnDiv. Std. Dev.

Avg. ORPE 1,372.4 134.89 1,603.8 49.11 1,537.2 317.13 1,362.9 1136.7 Avg. NIPE 32.41 28.31 60.02 33.83 -124.85 116.64 258.41 460.07 Avg. MARS 0.04 0.02 0.19 0.24 0.05 0.007 0.03 0.06 Avg. COUNTRY

3 2.38 1 0 2 0 3 6.69

Apart from the previous industries that are mentioned above, Table 22 illustrates the

Herfindahl indices to be measured as moderately concentrated through the years 2009 to

2006. This reasoning is due to the threshold of having a value in between 0.15 to 0.25

(Horizontal Merger Guidelines), meaning that competition would not be as low as in the

industries mentioned above. The manufacture of furniture industry is more competitive with

less dominant players. This could also be observed from the value of the concentration index,

which on average is 63.67% and is less than the average of the previous industries where

dominant players existed. Moreover apart from the manufacture of food products industry, the

entropy index is higher compared to other industries, which reveals greater uncertainty within

the industry.



2009 2008 2007 2006

Entropy Measure 1.702 1.755 1.729 1.736 Herfindahl Index 0.156 0.148 0.173 0.180 Relative Measure 0.114 0.117 0.115 0.116 CR4 0.623 0.616 0.658 0.649 The descriptive statistics for the manufacture of furniture industry in Table 23 depicts

the average operating revenue per employee to be 1,420.78 and the net income per employee

120.59. High standard deviation is present for the variables net income per employee, risk,

capital intensity, market share and the number of countries. According to the industry average

of operating revenue per employee, the horizontal and unrelated companies are above this

value although each strategy embraces only 2 companies (Appendix 9, Output 11-12).

Table 23: Descriptive statistics of furniture industry

Variable Obs. Mean Std. Dev. Min. Max. ORPE 15 1,420.78 757.68 403.27 3,536.95


48

Table 24: Correlations

According to Table 24, operating

revenue per employee is negatively correlated

with size, ratio, vertical integration and un-

diversification strategies. On the other side,

net income per employee performance

measure is negatively correlated with risk,

size, ratio, vertical integration, horizontal

integration, unrelated diversified strategies.

These correlations are consistent with the

analysis of Table 21 above, in which ORPE is

positively correlated with HI strategy, therefore having the highest value for this type of

strategy. NIPE is positively correlated with UD strategy and therefore has the highest value

under this strategy. Moreover for both of the performance measures the firm’s size, ratio, and

the vertical integration strategies are negatively correlated (Appendix 9, Output 13).

6.5.Manufacture of Machinery and Equipment

Finally, the manufacture of machinery and equipment industry has initially a sample

of 51 companies, in which 3 of them (Skako A/S, Svejsemaskinefabrikken Migatronic A/S

and Roblon A/S) have been eliminated due to being holding companies. The final sample

embraces 48 companies, with 11 vertically integrated, 10 horizontally integrated, 12 unrelated

diversified and 15 undiversified firms. It is observed that this industry involves the integration

strategies more evenly dispersed (Appendix 10, T. 15).

Table 25: Number of companies based on integration strategies Strategy VI HI UnRe. UnDiv. Total Num. of Firms 11 10 12 15 48 Based on the average analysis computed for the each integration strategy in Table 26,

the undiversified companies indicate the highest operating revenue per employee (2,619) and

NIPE 15 120.59 335.84 -207.32 1,240.93 RISK 15 9.49 10.05 0.62 43.37 SIZE 15 0.24 0.08 0.14 0.38 CINT 15 427.72 460.77 40.65 2,020.22 MARS 15 0.06 0.94 0.001 0.36 CPE 15 353.03 41.02 252.12 417.85 RATIO 15 30.99 14.14 10.50 63.67 COUNTRY 15 2.4 4.56 0 18

Correlation ORPE NIPE ORPE 1.00 NIPE 0.83 1.00 RISK 0.28 -0.08 SIZE -0.53 -0.14 CINT 0.73 0.85 MARS 0.42 0.27 CPE 0.35 0.05 RATIO -0.85 -0.49 VI -0.04 -0.16 HI 0.10 -0.07 UR 0.06 -0.30 UD -0.07 0.40 COUNTRY 0.75 0.84


49

the market share (0.047) on average; followed by unrelated diversified, vertically integrated

and horizontally integrated. The average ORPE for the whole sample is 1,749 and based on

this the only strategy above this mean is the un-diversification strategy. However, in terms of

net income per employee the unrelated companies are outperforming, and still the

undiversified companies are above the average NIPE of the whole sample (71.62). Moreover

the average countries that the companies are operating in is the same and highest for the

horizontally integrated and un-diversified companies (4). In general it could be underlined

that this industry is favoring the undiversified companies more in terms of operating revenue

per employee, compared to the other industries.



HI Std. Dev.

UnRe. Std. Dev.

UnDiv. Std. Dev.

Avg. ORPE 1,489.9 777.5 1,002.55 147.9 1,527.3 376.11 2,619. 2363.46 Avg. NIPE 20.22 56.84 -43.26 99.54 157.97 371.71 116.8 111.51 Avg. MARS 0.01 0.02 0.006 0.006 0.009 0.009 0.05 0.07 Avg. COUNTRY

1 1.30 4 3.76 2 1.30 4 4.67

The concentration indices presented in Table 27, indicate an un-concentrated industry

since the values for the Herfindahl index are generally below the threshold of 0.15. Therefore,

the industry embraces high competition with no dominant players. However, by observing the

trend of the HH index throughout the years, an increase would be noticed. Although the index

is still low and moderate concentration exists for the year 2009, competition has slightly

declined over time. Moreover, the concentration ratios for the 4 biggest companies indicate a

rise throughout the years, which could be due to the fall of competition. As mentioned before,

since the industry is favoring the undiversified companies and dominating the industry with

the highest number of firms (15) this high competition maybe the presence of these

undiversified companies.



2009 2008 2007 2006

Entropy Measure 2.472 2.752 2.950 2.944 Herfindahl Index 0.174 0.115 0.084 0.083 Relative Measure 0.053 0.059 0.063 0.063 CR4 0.662 0.551 0.458 0.464 In Table 28, the summary statistics of the machinery industry is presented with an

average operating revenue per employee of 1749.7 and net income per employee of 71.62.


50

The variables that have high volatility are the measures of net income per employee, capital

intensity, market share and the number of countries. According to the previous industry

descriptive statistics, all of the industries presented high volatility for the average net income

per employee, market share and number of countries. Since the operating revenue per

employee illustrated high volatility only in the manufacture of food industry, this performance

measure could be considered to be more representative and reliable compared to NIPE

(Appendix 10, Output 14-15).

Table 28: Descriptive statistics of machinery and equipment industry

As mentioned above, this industry has the highest performance measurement values

for the undiversified companies on average. This is supported by the correlation outputs

illustrated in Table 29. The operating revenue per employee value is positively correlated with

net income per employee, capital intensity,

market share, cost per employee, un-

diversification strategy and the number of

countries. The only difference for the net

operating per employee value is that; it is

positively correlated with the unrelated

diversification strategy as well. Since the level

of correlation with the unrelated

diversification (0.2385) is greater than the un-

diversification strategy (0.1458), the average

Table 29: Correlations net income per employee value is greater for the unrelated

diversification strategy (157.97) (Appendix 10, Output 16).

Variable Obs. Mean Std. Dev. Min. Max. ORPE 48 1749.71 1489.56 774.10 10297.62 NIPE 48 71.62 211.21 -243.94 1254.11 RISK 48 9.13 5.83 1.88 29.13 SIZE 48 0.20 0.05 0.12 0.39 CINT 48 421.30 475.98 50.79 2833.54 MARS 48 0.02 0.04 0.0002 0.26 CPE 48 387.96 83.05 110.56 617.50 RATIO 48 28.07 10.09 4.27 54.10 COUNTRY 48 2.69 3.40 0 18

Correlation ORPE NIPE ORPE 1.00 NIPE 0.32 1.00 RISK -0.08 -0.16 SIZE -0.23 -0.07 CINT 0.32 0.82 MARS 0.82 0.19 CPE 0.25 0.04 RATIO -0.65 -0.44 VI -0.10 -0.13 HI -0.26 -0.28 UR -0.09 0.24 UD 0.40 0.15 COUNTRY 0.10 0.13


51

7. INDUSTRY COMPARISONS

The analysis of the summary statistics of the 5 industries has given an insight on how

the industries could differ in terms of the strategies undertaken and their effects on corporate

performance. This section will aggregate the findings above, in order to designate the

differences by conducting a comparison among the industries (Appendix 11).

According to the number of firms in each industry, the dominant industries in the

sample are the manufacture of food products (37%) and the manufacture of machinery and

equipment industries (33%). Among the 5 industries, the food industry has the highest

average value of operating revenue per employee (3,701), which is followed by the chemicals

industry (3,095), pharmaceutical industry (1,948), machinery industry (1,749) and the

furniture industry (1,421). On the other hand, the pharmaceutical industry has the highest net

income per employee (496.13) on the average, which has a huge difference from the other

industries. However, recall that for all the industries the values of NIPE are highly volatile.

Moreover, the pharmaceutical industry preserves its leadership in having the highest average

values of number of countries the firms are operating in (17) and the market share (9.04%)

(Appendix 11, Graphs 1-5). In addition, the Appendix 11-Table 8 illustrates a summary of the

signs of positive and negative correlations between the performance measures and the

variables. According to this, almost all the industry average performance measures have a

positive correlation with the capital intensity, market share, cost per employee and horizontal

integration variables.

Since this study is differentiating the integration strategies for each of the industries

and analyzing the effects on performance measures, it is crucial to indicate which industry is

outperforming the others based on each strategy. Initially, the manufacture of food industry

preserves its highest value of operating revenue per employee in the vertical, horizontal and

unrelated integration strategies. This industry maybe more efficient in the sense of

coordinating, monitoring and enforcing the process of production more effectively and has

greater achievements from the scale, scope and learning economies from the perspectives of

vertical and horizontal integration (Sudarsanam, 2010). Moreover, the unrelated diversified

firms could be benefiting more from the reductions in transaction costs and the efficient use

of internal capital markets. On the other hand, the leader for the undiversified companies is

the highly concentrated manufacture of chemicals and chemical products industry. In

addition, from the perspective of net income per employee measure the manufacture of


52

pharmaceutical industry is having the highest values for vertical, horizontal, and unrelated

integration strategies, which indicates that compared to other firms under each type of

strategy the pharmaceutical industry can more efficiently utilize their employee resources in

order to generate profits for the company. However, recall that effective comparison of NIPE

should only be made between companies in similar industries (Money-Zine) and this analysis

with 11 companies may not be an effective representation for the whole pharmaceuticals

industry (Appendix 12).

In addition to these comparisons, it is worth to underline the differences of the average

concentration indices in order to have an overall understanding of the industries’ competition.

Although there is a huge difference of total average market shares between the pharmaceutical

industry and the remaining industries, the average concentration ratio of the 4 biggest players

in the markets do not present high variability among them. The highest CR4 is held by the

pharmaceutical industry (92%) and is followed by the chemicals industry (81%) the food

industry (69%), the furniture industry (64%) and the machinery industry (54%). This values

indicate the low competition in pharmaceuticals and chemicals industries were dominant

players have high market shares, whereas the decrease in this ratio indicates an increase in

competition and a reduction in market shares are observed. Moreover, based on the

Herfindahl index the chemicals industry is highly concentrated (0.4373), which indicates low

competition involving dominant players. This indication is also supported by the entropy

measure, which has the lowest value for the chemicals industry (1.1176). Since the entropy

measure is the inverse measure of HH index, it depicts that the lower values of this index will

reveal more certainty in the relationships of the firms with the buyers in the market (due to

lower competition). On the other side, the machinery industry’s HH index indicates a

competitive industry relative to the other industries, since it has the lowest value (0.1141).

Therefore, this industry has less dominant players compared to the others and the total market

shares of the first 4 companies (CR4) are the lowest. Finally, in order to make comparisons

among the industries, the entropy can be divided by the number of firms in the industry. In

that case, the food industry has the lowest value of RE (0.0397) and the pharmaceutical

industry has the highest (0.1353). This highlights that the firms in the food industry are

exposed to low competition (on a per company basis), while the pharmaceutical companies

are exposed to a high competitive environment (Appendix 13).

Finally, a general analysis of the whole sample can be demonstrated in order to

differentiate the highest performing strategies without taking the differences of industries into

account. Out of the 4 strategies, the horizontally integrated firms are attaining the highest


53

value for operating revenue per employee (3,444) and the unrelated companies with the net

income per employee (167) figures. Therefore the choice of integration strategy may be a

trade-off for the companies in the sense that different strategies may favor different effects on

performance outcomes. The highest market share is preserved by horizontally integrated

firms, and the number of countries that the firms operate in is greatly undertaken by the

unrelated diversifiers (Appendix 14).

8. EMPIRICAL FINDINGS AND THE DISCUSSION OF RESULTS

This section will introduce the findings of the regression models conducted separately

for the manufacture of food products and the manufacture of machinery and equipment

industries. The remaining industries do not have adequate number of companies to perform a

statistical analysis. The regression analysis will be based on the OLS regression model with

inclusion and exclusion of the interaction effects19. For each of the two industries the

dependent variables of operating revenue per employee (ORPE) and the net income per

employee (NIPE) performance measures will be used.

8.1.Manufacture of Food Industry

Table 30 summarizes the estimation of the non-interactive regression model for the

food products industry. Initially, the performance measure of operating revenue per employee

(ORPE) is taken as the dependent variable. For the hypotheses H1 and H3 (positive

moderating effect), the coefficients of VI and COUNTRY should be positive and significant,

and by H2 the coefficient of HI should be greater than UR’s and significant. This model

indicates that the vertical integration strategy has a negative effect on ORPE (-510.7), and is

not statistically significant at the 0.10 significance level. Therefore the hypothesis H1 is

rejected, in which the positive effect of vertical integration was to be tested. The dummy

variables of horizontal integration (HI) and unrelated diversification (UR) are statistically

significant at the 0.10 level, where the HI strategy is outperforming the UR diversification

strategy by 6408.24 units on the ORPE. Based on this analysis, the second hypothesis H2 is

not rejected. However, it is crucial to note that it cannot be determined whether this

significant and positive effect on performance leads firms to horizontally integrate or if

horizontal integration causes this high operating revenue per employee. Finally, the third

hypothesis H3 is tested if geographic diversification had a positive effect on firm performance.

The model indicates a positive coefficient for the variable COUNTRY, however statistically

19 Detailed explanation under section 4, Methodology.


54

insignificant. Therefore, H3 is rejected. The constant having a high significance is difficult to

interpret “but can be viewed roughly as a pure competitive equilibrium rate of return that

would be earned in the economist’s model of pure competition.” (Bettis, 1981, p. 388) In a

pure competitive industry the ‘economic profit’ in equilibrium would be zero, therefore here

the ‘economic profit’ can be taken as the minimum return necessary for a company to stay in

the business. Hence, the constant can be taken as the equilibrium profit for a purely

competitive firm, in an accounting sense. “The returns above this embodied in the other

coefficients indicate some degree of monopoly power.” (Bettis, 1981, p. 388). Moreover, the

other statistically significant variables are the size and the capital intensity (CINT). The size,

which is the natural logarithm of the average number of employees, has a significant and

positive effect on ORPE, meaning that a one unit increase in the natural logarithm of number

of employees will increase the operating revenue per employee by 31,113 DKK. The capital

intensity, which is the ratio of fixed assets to number of employees, depicts a significant

effect, in which a 10% increase of this ratio may increase the ORPE by 249. Finally, the entire

regression was highly significant based on the F-statistics, and R2 is 61% in which illustrates

the total variation of the sample Y-values that has been explained by the linear relationship

with the independent variables X (Appendix 15, Output 17).

Table 30: Manufacture of food products industry regression model (ORPE-dependent variable)

ORPE Coefficient Std. Err. T-statistic Significance (Constant) -9,084.94 3,480.5 -2.61 0.012* RISK -68.54 95.98 -0.71 0.479 SIZE 31,113.2 12,066.6 2.58 0.013* CINT 2.49 0.82 3.03 0.004* MARS -704.4 9796.3 -0.07 0.943 CPE 12.12 8.13 1.49 0.143 RATIO -83.71 64.60 -1.30 0.202 VI -510.8 1,867.3 -0.27 0.786 HI 3,025.9 1,372.9 2.20 0.033* UR -3,382.3 1,881.8 -1.80 0.079* COUNTRY 128.71 124.55 1.03 0.307 Number of Obs.= 54, F-statistics= 6.82 (significance= 0.000) R2= 0.61, adjusted R2= 0.52 *Significant at the 0.10 level.

Moreover, the model has been tested with the inclusion of the interaction terms, in

which it “was designed to investigate more fully the reasons for differences in performance

among different diversification strategies.” (Bettis, 1981, p. 384) This model is constructed

with the forward stepwise procedure, in which suitable subsets of independent variables are


55

chosen from the total regression model. In addition to this, the Appendix 16-Output 20

encloses the simple OLS regression model with the interaction effects; however since the

sample size is small with 48 companies, having 33 regression variables will be

misrepresentative of the estimations.

Table 31 presents the significant interaction terms of the final model. These

interaction terms can be interpreted as; the reasons of high performance for horizontally

integrated firms were the size of the firm (SIZE), and their capital intensity (CINT). The low

performance of the HI companies would be due to the cost per employee, which an increase

of this average would lead to a decline in operating revenue per employee. This negative

correlation is observed in the correlation matrix of the food industry in Appendix 7, where all

the integration strategies apart from the unrelated diversification have negative correlations

with the cost per employee measure. Therefore the significant and positive effect of CPE

(14.42) could be due to this correlation with the unrelated diversification strategy, although

this model does not represent UR as significant. Moreover, the cost of employee to operating

revenue ratio (RATIO) has a negative significant effect on performance. This could be

expected since the correlation matrix is depicting a negative relationship between the RATIO

and ORPE, and an increase in RATIO will indicate that the costs are greater than the

revenues. The F-statistic for the entire regression is highly significant, with a R2 value of 90%


Table 31: Interactive regression model (ORPE) for manufacture of food products industry ORPE Coefficient Std. Err. T-statistic Significance (Constant) 189.74 1,209.9 0.16 0.876 (CINT)(HI) 7.85 0.75 10.42 0.000* (CPE)(HI) -36.54 7.22 -5.06 0.000* (SIZE)(HI) 44,390.4 15,424.4 2.88 0.006* RATIO -151.05 25.68 -5.88 0.000* CPE 14.42 2.81 5.12 0.000* Number of Obs.= 54, F-statistics= 88.34 (significance= 0.000) R2= 0.90, adjusted R2= 0.89 *Significant at the 0.10 level. In terms of average net operating income per employee (NIPE) as the dependent

variable, Table 32 presents the non-interactive regression model. Here, the only significant

values to be observed are the constant and the size. The decrease in the number of significant

variables could be due to the reason that operating revenue per employee is more reliable and

explanatory as a performance measurement compared to the net income per employee value.


56

Therefore, all the three hypotheses are rejected since the variables are not statistically

significant, although the coefficients have a positive value for VI, HI, UR and COUNTRY.

Moreover the horizontal integration strategy has a higher coefficient compared to the

unrelated diversification strategy. The entire regression is significant based on the F-

statistics, however the R2 is lower compared to the previous model (35%) (Appendix 15,

Output 18).

Table 32: Manufacture of food products industry regression model (NIPE-dependent

variable)

NIPE Coefficient Std. Err. T-statistic Significance (Constant) -390.66 135.38 -2.89 0.006* RISK 2.82 3.73 0.76 0.454 SIZE 819.82 469.36 1.75 0.088* CINT 0.02 0.03 0.73 0.472 MARS 217.24 381.05 0.57 0.572 CPE 0.38 0.32 1.19 0.239 RATIO 3.59 2.51 1.43 0.160 VI 9.42 72.63 0.13 0.897 HI 72.07 53.40 1.35 0.184 UR 53.50 73.20 0.73 0.469 COUNTRY 0.31 4.84 0.06 0.949 Number of Obs.= 54, F-statistics= 2.36 (significance= 0.0250) R2= 0.35, adjusted R2= 0.20 *Significant at the 0.10 level.

Table 33 demonstrates the forward stepwise interactive regression model. Compared

to the simple OLS regression presented above, this model has specified more significant

variables by adding and removing the variables based on their significance level. Here, the

high performance of the unrelated diversified firms is dependent upon their capital intensity

(CINT), cost of employee to operating revenue ratio (RATIO) and their market share

(MARS). The firms may be keen on diversifying into unrelated areas when their fixed assets,

costs and market shares are high or initially being UR diversified may be the outcome of these

positive interaction affects. The causality of the impacts cannot be determined strictly. For

horizontally integrated companies, their low performance will be due to an increase in the

variable RATIO. Moreover, the low performance of an increase in the number of countries

(increasing geographic diversification) may depend on the effect of capital intensity (CINT).

Compared to other industries such as oil production, telecommunications etc., the food

industry could be considered as having a low capital intensity. Based on this determination, an

increase in the number of countries may increase the number of employees being hired more

than the need of capital, which overall decreases capital intensity. Besides the effects of the


57

interactive variables, this model underlines the significance of unrelated and horizontally

diversified variables. Here, horizontal integration is outperforming the unrelated diversified

companies by having a large and positive effect on the net income per employee performance

measure (287.11> (-702.44)). This significance is supporting the hypothesis H2. The model is

significant with a high value of F-statistics and an R2 of 69%, lower than the interactive

model for ORPE (Appendix 16, Output 21).

Table 33: Interactive regression model (NIPE) for manufacture of food products

industry

NIPE Coefficient Std. Err. T-statistic Significance (Constant) 56.03 21.16 2.65 0.011* UR -702.44 120.82 -5.81 0.000* HI 287.11 69.66 4.12 0.000* (CINT)(UR) 0.20 0.04 5.75 0.000* (RATIO)(HI) -16.37 4.03 -4.07 0.000* (CINT)(COUNTRY) -0.007 0.003 -2.36 0.023* (RATIO)(UR) 24.79 3.13 7.92 0.000* (MARS)(UR) 2,585.81 1,490.91 1.73 0.090* Number of Obs.= 54, F-statistics= 14.69 (significance= 0.0250) R2= 0.69, adjusted R2= 0.64 *Significant at the 0.10 level.

8.2.Manufacture of Machinery and Equipment Industry

The second industry, the manufacture of machinery and equipment is illustrated in the

non-interactive regression model in Table 34. The average operating revenue per employee

(ORPE) is taken as the dependent variable initially, and based on the output the size (SIZE),

market share (MARS), cost per employee (CPE), the ratio of cost of employee to operating

revenue (RATIO) and the horizontal integration (HI) variables are statistically significant at

the 0.10 level. The variables of size have been significant and positive for both the food and

the machinery industry. Among the significant variables, the market share (MARS) stands out

with its high positive significance (t-value, 10.84) and this is supported by the high correlation

with the dependent variable ORPE (0.53). The reason of this significance could be due to the

high competition within the industry (average HH index, 0.1141), where increasing a

company’s share in the market may result in an effective increase in performance. The model

statistically indicates that a 1% increase in market share will lead an increase of 25,497 DKK

in operating revenue per employee. In addition, H1 and H2 are rejected, since the vertical

integration (VI) and the COUNTRY variables are not statistically significant, although they

embrace a positive coefficient. On the other side, the horizontal integration strategy is

statistically significant at the 0.10 level with a greater coefficient than the unrelated


58

diversification strategy. However, the UR variable is statistically insignificant. To sum up,

this model is highly significant with a high value of F-statistics and with a 90% value of R2


Table 34: Manufacture of machinery and equipment industry regression model (ORPE

dependent variable)

ORPE Coefficient Std. Err. T-statistic Significance (Constant) -181.74 538.06 -0.34 0.737 RISK -3.45 16.83 -0.21 0.839 SIZE 6,992.5 1,949.1 3.59 0.001* CINT -0.27 0.21 -1.31 0.195 MARS 25,497.6 2,353.2 10.84 0.000* CPE 5.85 1.07 5.49 0.000* RATIO -82.14 11.90 -6.90 0.000* VI 190.43 252.60 0.75 0.456 HI 510.76 291.89 1.75 0.088* UR 216.24 254.75 0.85 0.401 COUNTRY 9.91 31.66 0.31 0.756 Number of Obs.= 48, F-statistics= 32.07 (significance= 0.000) R2= 0.90, adjusted R2= 0.87 *Significant at the 0.10 level.

The same model is tested with the inclusion of the interaction terms and the significant

variables are presented in Table 35, which are the outputs of the forward stepwise regression

model. The cost per employee (CPE), the cost of employee to operating revenue ratio

(RATIO), and the size (SIZE) are statistically significant and positive as the previous model

presented above. The number of countries has an interactive positive effect with the market

share and a negative effect with the capital intensity. It can be interpreted as the high

performance of geographically diversified companies can be due to high market shares and

the low performance would be attributable to capital intensity. And finally, the horizontal

integration strategy’s high performance depends on the cost per employee, however with a

relatively lower significance (0.087) compared to other variables significance levels (0.000).

This model is highly significant with an F-statistics of 75.13 and a R2 of 92% (Appendix 18,

Output 24).

Table 35: Interactive regression model (ORPE) for manufacture of machinery and

equipment industry regression model

ORPE Coefficient Std. Err. T-statistic Significance (Constant) 713.72 419.07 1.70 0.096* (MARS)(COUNTRY) 4,437.4 338.46 13.11 0.000* RATIO 82.99 9.68 -8.58 0.000* CPE 5.84 0.87 6.73 0.000* SIZE 4,153.1 1,435.4 2.89 0.006*


59

(CPE)(HI) 0.94 0.54 1.75 0.087* (CINT)(COUNTRY) -0.09 0.02 4.89 0.000* Number of Obs.= 48, F-statistics= 75.13 (significance= 0.000) R2= 0.92, adjusted R2= 0.90 *Significant at the 0.10 level. Finally, the average net income per employee (NIPE) performance measure is taken as

the dependent variable in estimating the regression model of the machinery industry. In Table

36, the significant values that are observed to be are the capital intensity (CINT) and the

vertical integration strategy (VI). However the vertical integration strategy (-89.9) has a

negative impact on the firms’ performance in terms of NIPE, which is not supporting the first

hypothesis H1. This negative impact could be due to the decreases of the net income figures of

the companies over the 5 year period20. Moreover, the downsides of vertical integration could

be another reason for the negative effect which are the opportunism due to interdivisional

rivalry and the increase in influence costs (Sudarsanam, 2010), whereas the significance could

be due to having greater experience in a specific type of technology (Leiblein & Miller, 2003)

since machinery and equipments industry is based on more technological know-how

compared to the food industry. The remaining hypotheses are not supported as well, since the

HI, UR and COUNTRY variables are not significant and apart from the unrelated diversified

strategy, their coefficients are negative. And, this last non-interactive regression model has

high significance in terms of its F-statistics and a high value of R2 which is 76% (Appendix

17, Output 23).

Table 36: Manufacture of machinery and equipment industry regression model (NIPE-

dependent variable)

NIPE Coefficient Std. Err. T-statistic Significance (Constant) 73.15 112.42 0.65 0.519 RISK -2.93 3.52 -0.83 0.410 SIZE 468.45 407.24 1.15 0.257 CINT 0.37 0.04 8.59 0.000* MARS -305.96 491.66 -0.62 0.538 CPE -0.24 0.22 -1.08 0.288 RATIO -2.55 2.49 -1.03 0.311 VI -89.90 52.78 -1.70 0.097* HI -50.38 60.99 -0.83 0.414 UR 18.19 53.23 0.34 0.734 COUNTRY -9.55 6.61 -1.44 0.157 Number of Obs.= 48, F-statistics= 12.78 (significance= 0.000) R2= 0.76, adjusted R2= 0.71 *Significant at the 0.10 level.

20 In general vertically integrated companies have reported negative net income values in the last years, while number of employees were not highly volatile and somewhat stable.


60

The last interactive regression model is illustrated in Table 37, where the output is

obtained by regressing the net income per employee performance measure to its individual

variables and interactive terms. Based on the outcome, the capital intensity, ratio, and the

constant are statistically significant at the 0.10 level. The reasons of the high performance of

the unrelated diversified firms are the capital intensity and the market share. Moreover, the

performance of the horizontally integrated companies is dependent significantly upon the

market share that the firm holds. Therefore, this model is consistent with the ORPE regression

models presented above, in the sense that the machinery industry gives more emphasis on the

market share due to being a competitive industry. As in the previous interactive regression

models, this model has a significant value of F-statistic, with an R2 of 84% (Appendix 18,

Output 26).

Table 37: Interactive regression model (NIPE) for manufacture of machinery and

equipment industry

NIPE Coefficient Std. Err. T-statistic Significance (Constant) 145.22 52.82 2.75 0.009* CINT 0.13 0.05 2.71 0.010* (CINT)(UR) 0.29 0.05 5.50 0.000* RATIO -5.04 1.46 -3.45 0.001* (MARS)(UR) -7,295.8 2,730.5 -2.67 0.011* (MARS)(HI) -7,234 3,690.6 -1.96 0.057* Number of Obs.= 48, F-statistics= 42.99 (significance= 0.000) R2= 0.84, adjusted R2= 0.82 *Significant at the 0.10 level.

8.3.Discussion of Results

The empirical outcomes illustrate that the effects of the variables on the performance

measurements are varying based on the type of industry. These differences “are related to the

firm’s environment, and specifically to the characteristics of the markets in which they

participate.” (Montgomery & Christensen, 1981, p. 328) Initially, in order to see the

similarities of the regression models’ outcomes between the two industries, the same

significant non-interactive and interactive terms are highlighted.

• Non-interactive model

o ORPE as dependent variable: Horizontal integration (HI) and the firm size

(SIZE)

o NIPE as dependent variable: None

• Interactive model


61

o ORPE as dependent variable: (Cost per employee)(Horizontal integration),

cost per employee and cost of employee to operating revenue ratio

o NIPE as dependent variable: (Capital intensity)(Unrelated diversification) and

(Market share)(Unrelated diversification)

From the perspective of the operating revenue per employee, the positive and

significant integration strategy for the two industries is horizontal integration, in which it is

outperforming the unrelated diversification strategy. This result is consistent with the findings

of Rumelt (1982), Bettis (1981) who indicate that the related diversified companies are

outperforming the unrelated diversified firms in terms of corporate performance. In the non-

interactive model, the two industries did not have a common variable that has been significant

at the 0.10 level for the net income per employee performance measure. This could highlight

that operating revenue per employee is more explanatory and relevant in explaining the

relations with integration strategies and the control variables. In addition, in the interactive

regression models, the interactions with the horizontal and un-diversification strategies were

capturing more significance for the two industries and in general the control variables of cost

per employee, capital intensity, and the firms’ size were of major interest in explaining the

corporate performance.

Besides these significant variables, it is crucial to note that the findings do not

underline significant effects for the vertical integration (VI) and geographical diversification

(COUNTRY) strategies. Previous findings indicated that vertical integration occurred when

the investment involved high specificity in knowledge, assets and know-how (Monteverde &

Teece, 1982). A rise of complexity and specialization of the inputs would increase the

probability to vertically integrate (Masten, 1984). According to these classifications, the

inputs that the firms’ are internalizing may not be specific and critical enough to capture a

significant impact on the performance measures. The only significant and negative effect of

vertical integration has been observed on the net income per employee measure, which was

for the manufacture of machinery and equipment industry. On the other side, the reason of the

inability to capture a significant effect could be due to the small sample size. For instance, the

number of vertically integrated companies is 6 and 11 for manufacture of food and machinery

industries respectively.


62

Based on the summary statistics presented above, these two industries’ average

performances could be analyzed more in detail, in accordance with the empirical results.21

The manufacture of machinery and equipment industry has the highest average operating

revenue per employee figure for undiversified companies (2,619). Since it is an un-

concentrated industry with no or few dominant players, the presence of many undiversified

companies is reasonable. Moreover, when the size (natural log of number of employees) of

the undiversified firms was compared to the remaining companies within the industry, they

were smaller. This may support the findings of Nathanson & Cassano (1982) which indicated

that smaller firms were performing better compared to larger firms in the categories of no

diversification or extremely high diversification. Moreover, the food industry has the highest

operating revenue per employee (5,016) and market share (0.04) for its horizontally integrated

companies, within a highly concentrated market. The related diversifiers “appear to be more

profitable in part because they operated in very profitable, highly concentrated markets, and

were able to acquire large shares in those markets.” (Montgomery & Christensen, 1981, p.

339) The ownership of sufficient level of skills and resources are crucial in these high

opportunity markets in explaining the companies’ above-average market shares, due to

expanding into related areas. Therefore, the combination of the market opportunity and the

ability to take advantage of that opportunity leads to successful performance outcomes

(Montgomery & Christensen, 1981).

In addition to operating revenue per employee measures, the net income per employee

values indicate the highest values to be attributed to the unrelated diversified firms for both of

the industries. Since net income (in its general form) is the revenues minus expenses, the

unrelated firms may have the ability to cover their costs more efficiently, by creating value by

maintaining an effective performance compared to the external capital markets. This

efficiency can lead the diversified companies to realize economies of scope, reduce risks and

uncertainty, and reduce transaction costs with the means of internal capital markets. However,

the profitability of the primary business has an important role on the decision to diversify

(Lipczynski et al., 2005). “A conglomerate that reallocates capital from a less profitable core

activity to a more profitable non-core activity contributes to an improvement in the efficiency

of capital allocation.” (p. 577)

21 Recall that these two industries have a sample size greater than the remaining industries, indicating a more accurate comparison.


63

9. CONCLUSION

Both the summary statistics and the empirical results tried to underline the differences

between the 5 industries, in terms of the companies’ choices of integration strategies and the

effects on their corporate performance. According to the descriptive statistics, the choice and

the dominance of strategies are varying based on the industry that the firms are operating in.

The manufacture of food products industry is favoring the vertical, horizontal and unrelated

integration strategies in terms of average operating revenue per employee performance

measure. This outperformance could suggest that the food industry is encouraging the

vertically integrated firms in having the efficiencies of coordinating, monitoring and the

enforcement in the process of production (Sudarsanam, 2010). Moreover, the companies in

this sector could be reaching the efficiencies of scale, scope and learning economies more

favorably for horizontally integrating. Within this industry, the horizontally integrated firms

are outperforming the unrelated diversified companies; however the reason of having the

highest ORPE out of the remaining industries for this type of diversification could be the

effective reductions of transaction costs and making efficient use of internal capital markets.

On the other hand, the undiversified companies in the highly concentrated manufacture of

chemicals industry have on average highest value of ORPE performance. Apart from the

chemicals, food and pharmaceuticals industry; the manufacture of machinery and furniture

industries are subject to higher competition and lower values of performance measures are

observed for the integration strategies chosen.

The general empirical evidence suggested that high levels of asset specificity and

know-how may lead the firms to vertically integrate (Monteverde & Teece, 1982) in order to

prevent the hold-up problem and extensive quasi-rents (Grossman & Hart, 1986; Williamson,

1971). However, the manufacture of food and the manufacture of machinery and equipment

industries did not underline a significant positive effect of vertical integration strategy, which

could be due to not having critical relation-specific assets that would significantly affect the

performance of the companies. On the other side, both of the industries have illustrated high

significant positive effect for the horizontal integration strategy, which has been consistent

with the findings of Rumelt (1982), Bettis (1981), and Montgomery (1994). However, based

on the differences of market structures the effects of horizontal integration is not the same, in

which higher significance is observed for the food industry. Finally, the empirical evidence on

the effect of geographic diversification generally indicated a positive relationship between the


64

geographic scope and firm’s performance (Delios & Beamish, 1999; Lepetit et al., 2004;

Ravichandran, 2009). This study could not observe any significant outcomes for the number

of countries that the firms are operating in, although the coefficients were positive for the

ORPE measure.

Therefore, the effects of the strategy of vertical integration and geographic

diversification could not reach a specific conclusion, which may be attributed to the limited

observation sample used in the study. The main reason has been the difficulty of finding

applicable values for the variables used in the analysis. Future studies could increase the

number of industries with different measures of profitability and diversification strategies and

the inclusion of more control variables such as R&D intensity and advertising. “As the

separate research traditions that study corporate economic performance become integrated,

both research and managerial practice will be enriched.” (Montgomery & Christensen, 1981,

p. 340)


65

REFERENCES

Articles

Baker, George; Gibbons, Robert & Murphy, Kevin J. (2002). Relational Contracts and the

Theory of the Firm. In: Quarterly Journal of Economics. Vol. 117, Iss. 1, p. 39-84.

Bettis, Richard A. (1981). Performance Differences in Related and Unrelated Diversified

Firms. In.: Strategic Management Journal. Vol. 2, Iss. 4, p. 379-393.

Bettis, Richard A. & Hall, William.K. (1982). Diversification Strategy, Accounting

Determined Risk and Accounting Determined Return. In: Academy of Management Journal.

Vol, 25, Iss. 2, p. 254-264.

Capar, Nejat. (2009). An Analysis of the Relationship Between International Diversification,

Product Diversification, Firm Resources and Performance. In: Academy of Management

Annual Meeting Proceedings. P. 1-6.

Capon, N.; Hulbert, J. M.; Farley, J.U.; Martin, L.E. (1988). Corporate Diversity and

Economic Performance: The Impact of Market Specialization. In: Strategic Management

Journal, Vol. 9, Iss. 1, p. 61-74.

Chakravarty, Satya R. (1998). Efficient Horizontal Mergers. In: Journal of Economic Theory.

Vol. 82, Iss. 1, p. 277-289.

Chang, Shao-Chi & Wang, Chi-Feng. (2007). The Effect of Product Diversification Strategies

on the Relationship Between International Diversification and Firm Performance. In: Journal

of World Business. Vol. 42, Iss. 1, p. 61-79.

Coase, R. H. (1937). The Nature of the Firm. In: Economica. Vol. 4, Iss. 16, p. 386-405.

Colangelo, Giuseppe. (1995). Vertical vs. Horizontal Integration: Pre-emptive Merging. In:

The Journal of Industrial Economics. Vol. 43, Iss. 3, p. 323-337.

Delios, Andrew & Beamish, Paul W. (1999). Geographic Scope, Product Diversification and

the Corporate Performance of Japanese Firms. In: Strategic Management Journal. Vol. 20, Iss.

8, p. 711-727.

Denis, David J.; Denis, Diane K. & Yost, Keven. (2002). Global Diversification, Industrial

Diversification, and Firm Value. In: The Journal of Finance. Vol. 57, Iss. 5, p. 1951-1979.


66

Fan, Joseph P.H., & Goyal, Vidk an K. (2006). On the Patterns and Wealth Effects of

Vertical Mergers. In: Journal of Business. Vol. 79, Iss. 2, p. 877-902.

Fisher, N. & Hall, G.R. (1969). Risk and Corporate Rates of Return. In: Quarterly Journal of

Economics. Vol. 83, Iss. 1, p. 79-92.

Geringer, Michael J.; Tallman, Stephen & Olsen, David M. (2000). Product and International

Diversification among Japanese Multinational Firms. In: Strategic Management Journal. Vol.

21, Iss. 1, p. 51-80.

Grossman, Sanford J. & Hart, Oliver D. (1986). The Costs and Benefits of Ownership: A

Theory of Vertical and Lateral Integration. In: Journal of Political Economy. Vol. 94, Iss. 4, p.

691-719.

Hitt, Micheal A.; Hoskisson, Robert E. & Kim, Hicheon. (1997). International

Diversification: Effects on Innovation and Firm Performance in Product-Diversified Firms.

In: Academy of Management Journal. Vol. 40, Iss. 4, p. 767-798.

Hubbard, Thomas N. (2008). Viewpoint: Empirical Research on Firms’ Boundaries. In:

Canadian Journal of Economics. Vol. 41, Iss. 2, p. 341-359.

Hsiao, Cheng. (2005). Why Panel Data? In: Singapore Economic Review. Vol. 50, Iss. 2, p.

143-154.

Jacquemin, Alexis P. & Berry, Charles H. (1979). Entropy Measure of Diversification and

Corporate Growth. In: The Journal of Industrial Economics. Vol. 27, Iss. 4, p. 359-369.

Joskow, Paul, L. (1985). Vertical Integration and Long Term Contracts: The Case of Coal-

burning Electric Generating Plants. In: Journal of law, Economics & Organization. Vol. 1, Iss.

1, p. 33-81.

Kahloul, Ines & Hallara, Slaheddine. (2010). The Impact of Diversification on Firm

Performance and Risk: An Empirical Evidence. In: International Research Journal of Finance

and Economics. Iss. 35, p. 150-162.

Kim, Young Sang & Mathur, Ike. (2008). The Impact of Geographic Diversification on Firm

Performance. In: International Review of Financial Analysis. Vol. 17, Iss. 4, p. 747-766.


67

Kim, Jongwook & Mahoney, Joseph T. (2005). Property Rights Theory, Transaction Cost

Theory, and Agency Theory: an Organizational Economics Approach to Strategic

Management. In: Managerial and Decision Economics. Vol 26. Iss. 4, p. 223-242.

Kiker, Scott D. & Banning, Kevin C. (2008). How Important is Diversification? A Meta-

Analytic Review of the Diversification/Firm Performance Relationship. In: Southern Business

& Economic Journal. Vol. 31, Iss. 1/2, p. 19-26.

Kumar, Krishna B.; Rajan, Raghuram G.; Zingales, Luigi. (1999). What Determines Firm

Size? In: Working Papers-Yale School of Management’s Economic Research Network, p. 1-

54.

Lafontaine, Francine & Slade, Margaret. (2007). Vertical Integration and Firm Boundaries:

The Evidence. In: Journal of Economic Literature. Vol. 45, Iss. 3, p. 629-685.

Lang, Larry H. P. & Stulz, Rene M. (1994). Tobin’s q, Corporate Diversification, and Firm

Performance. In: Journal of Political Economy. Vol. 102, Iss. 6, p. 1248-1279.

Lecraw, Donald J. (1984). Diversification strategy and Performance. In: Journal of Industrial

Economics. Vol. 33, Iss. 2, p. 179-198.

Leiblein, Michael J. & Miller, Douglas J. (2003). An Empirical Examination of Transaction

and Firm-Level Influences on the Vertical Boundaries of the Firm. In: Strategic Management

Journal. Vol. 24, Iss. 9, p. 839-859.

Lepetit, Laetitia; Patry, Stephanie & Rous, Philippe. (2004). Diversification versus

Specialization: An event study of M&A’s in the European banking industry. In: Applied

Financial Economics. Vol. 14, Iss. 9, p. 663-669.

Lindsey, Matthew D. & Neeley, Concha R. (2010). Building Learning Curve and Script

Theory Knowledge with Lego. In: Marketing Education Review. Vol. 20, Iss. 1, p. 71-75.

McGahan, Anita M. & Porter, Micheal E. (1997). How Much Does Industry Matter, Really?

In: Strategic Management Journal. Vol. 8, p. 15-30.

Matraves, Catherine & Rondi, Laura. (2007). Product Differentiation, Industry Concentration

and Market Share Turbulunce. In: International Journal of the Economics of Business. Vol.

14, Iss. 1, p. 37-57.


68

Masten, Scott, E. (1984). The Organization of Production: Evidence from the Aerospace

Industry. In: Journal of Law and Economics. Vol. 27, Iss. 2, p. 403-418.

Miller, D.J. (2006). Technological Diversity, Related Diversification, and Performance. In:

Strategic Management Journal. Vol. 27, Iss. 7, p. 601-619.

Montgomery, Cynthia A. (1994). Corporate Diversification. In: Journal of Economic

Perspectives. Vol. 8, Iss. 3, p. 163-178.

Montgomery, Cynthia A. & Wernerfelt, Birger. (1988). Diversification, Ricardian Rents, and

Tobin’s q. In: Journal of Economics. Vol. 19, p 623-632.

Montgomery, Cynthia A. & Christensen, Kurt H. (1981). Corporate Economic Performance:

Diversification Strategy Versus Market Structure. In: Strategic Management Journal. Vol. 2,

Iss. 4, p. 327-343.

Monteverde, Kirk & Teece, David J. (1982). Supplier Switching Costs and Vertical

Integration in the Automobile Industry. In: Bell Journal of Economics. Vol. 13, Iss, 1, p. 206-

213.

Nathanson, Daniel & Cassano, James. (1982). Organization, diversity and Performance. In:

Wharton Magazine, p. 19-26.

Panzar, John C. & Willig, Robert D. (1981). Economies of Scope. In: American Economic

Review. Vol. 71, Iss. 2, p. 268-272.

Porter, Michael E. (1976). Please Note Location of Nearest Exit: Exit Barriers and Planning.

In: California Management Review. Vol. 19, Iss. 2, p. 21-33.

Prahalad, C. K. & Bettis, Richard A. (1986). The Dominant Logic: A New Linkage Between

Diversity and Performance. In: Strategic Management Journal. Vol. 7, Iss. 6, p. 485-501.

Ravichandran, T.; Liu, Yu; Han, Shu & Hasan, Iftekhar. (2009). Diversification and Firm

Performance: Exploring the Moderating Effects of Information Technology Spending. In:

Journal of Management Information Systems. Vol. 25, Iss. 4, p. 205-240.

Rumelt, Richard P. (1982). Diversification Strategy and Profitability. In: Strategic

Management Journal. Vol. 3, Iss. 4, p. 359-369.


69

Santalo, Juan & Becerra, Manuel. (2008). Competition from Specialized Firms and the

Diversification-Performance Linkage. In: The Journal of Finance. Vol. 63, Iss. 2, p. 851-883.

Schoar, Antoinette. (2002). Effects of Corporate Diversification on Productivity. In: The

Journal of Finance. Vol. 57, Iss. 6, p. 2379-2403.

Walker, Gordon & Weber, David. (1984). A Transaction Cost Approach to Make-or-Buy

Decisions. In: Administrative Science Quarterly. Vol. 29, Iss. 3, p. 373-391.

Weiss, L.W. (1963). Factors in Changing Concentration. In: Review of Economics and

Statistics. Vol. 45, p. 70-77.

Wiersema, Margarethe F. & Bowen, Harry P. (2008). Corporate Diversification: The Impact

of Foreign Competition, Industry Globalization, and Product Diversification. In: Strategic

Management Journal. Vol. 29. Iss. 2, p. 115-132.

Williamson, Oliver E. (1971). The Vertical Integration of Production: Market Failure

Considerations. In: American Economic Review. Vol. 61, Iss. 2, p. 112-123.

Books

Agresti, Alan & Finlay, Barbara. (1997). Statistical Methods for the Social Sciences. 3rd

Edition. Prentice Hall.

Bain, J.S. (1959). Industrial Organization. Publisher: New York, John Wiley.

Besanko, David; Dranove, David; Shanley, Mark & Schaefer, Scott. (2007). Economics of

Strategy. 4th Edition. p. 75-186.

Cosh, A.; Hughes, A. & Singh, A. (1980). The Determinants and Effects of Mergers: an

International Comparison. Cambridge, MA: Oelgeschlager, Gun & Hanin.

Harnett, Donald L. & Soni, Ashok K. (1991). Statistical Methods for Business and

Economics. 4th Edition, Publisher: Addison-Wesley Publishing Company

Johnson, Gerry; Scholes, Kevan & Whittington, Richard. (2008). Exploring Corporate

Strategy: Text & Cases. 8th Edition. Prentice Hall.

Lichtenberg, Frank R. (1992). Corporate Takeovers and Productivity. Publisher: Cambridge,

MIT Press.


70

Lipczynski, John; Wilson, John & Goddard, John. (2005). Industrial Organization:

Competition, Strategy, Policy. 2nd Edition. Publisher: Pearson Education, p. 210-619.

Lipczynski, John & Wilson, John. (2001). Industrial Organization: An Analysis of

Competitive Markets. 1st Edition. Publisher: Pearson Education, p. 103-133.

Marris, R. (1964). The Economic Theory of Managerial Capitalism. Publisher: London,

Macmillan (1920).

Meeks, G. (1977). Disappointing Marriage: A Study of the Gains from Merger. Publisher:

Cambridge University Press.

Penrose, E. (1995). The Theory of the Growth of the Firm. 3rd Edition . Publisher: Oxford,

Oxford University Press.

Ravenscraft, D.J. & Scherer, F. (1987). Mergers, Sell Offs and Economic Efficiency.

Publisher: Washington, DC, The Brookings Institution.

Rondi, L.; Sembenelli, A. & Ragazzi, E. (1996). Determinants of diversification patterns, in

Davies, S. and Lyons, B. (eds) Industrial Organization in the European Union. Publisher:

Oxford, Oxford University Press, Ch. 10.

Rumelt, Richard P. (1974). Strategy, Structure, and Economic Performance. Division of

Research, Graduate School of Business Administration, Harvard University.

Silverman, Brian S. (2002). Technological Resources & the Logic of Corporate

Diversification. Publisher: London, New York. E-book.

Sudarsanam, Sudi. (2010). Creating Value from Mergers and Acquisitions: The Challenges.

2nd Edition. p. 124-216.

Williamson, Oliver E. (1975). Markets and Hierarchies. Publisher: New York, Free Press.

Web Sites

Engineering Statistics Handbook, “Measures of Skewness and Kurtosis”,

http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm, Retrieved: 5 June 2011.

Horizontal Merger Guidelines. (2010). U.S. Department of Justice and the Federal Trade

Commission, http://www.justice.gov/atr/public/guidelines/hmg-2010.html part 5.3

http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm

http://www.justice.gov/atr/public/guidelines/hmg-2010.html


71

Investopedia (2011), “Operating Performance Ratios”

http://www.investopedia.com/university/ratios/operating-performance/ratio2.asp, Retrieved: 5

June 2011

Investopedia (2011), “Market Share”, http://www.investopedia.com/terms/m/marketshare.asp,

Retrieved: 5 June 2011

InvestorWords: Glossary, “Total Cost”, http://www.investorwords.com/5006/total_cost.html,

Retrieved : 5 June 2011

Money-Zine, “Income per Employee”, http://www.money-zine.com/Definitions/Investing-

Dictionary/Income-per-Employee/, Retrieved: 5 June 2011

VentureLine: Accounting Dictionary, “Proprietary Asset Definition”

http://www.ventureline.com/accounting-glossary/P/proprietary-asset-definition/, Retrieved: 5

June 2011

Wikipedia: The Free Encyclopedia, “Cross-Sectional Data”, Wikipedia Foundation,

http://en.wikipedia.org/wiki/Cross-sectional_data, Retrieved: 6 June 2011

Wikipedia: The Free Encyclopedia, “Concentration Ratio”, Wikipedia Foundation,

http://en.wikipedia.org/wiki/Concentration_ratio#cite_note-amos-0, Retrieved: 7 June 2011

http://www.investopedia.com/university/ratios/operating-performance/ratio2.asp

http://www.investopedia.com/terms/m/marketshare.asp

http://www.investorwords.com/5006/total_cost.html

http://www.money-zine.com/Definitions/Investing-Dictionary/Income-per-Employee/

http://www.money-zine.com/Definitions/Investing-Dictionary/Income-per-Employee/

http://www.ventureline.com/accounting-glossary/P/proprietary-asset-definition/

http://en.wikipedia.org/wiki/Cross-sectional_data


72

List of Appendices

Appendix 1. Orbis snapshot and the search strategy .......................................................... 72

Appendix 2. List of companies based on industries .......................................................... 73

Appendix 3. The IO table from statbank.dk for the food industry ..................................... 76

Appendix 4. A representation on how the industries have been identified ....................... 81

Appendix 5. The ROA value for Novo Nordisk A/S through 2005-2009 ......................... 82

Appendix 6. The summary statistics for the manufacture of basic pharmaceuticals and

pharmaceutical preparations industry ................................................................................. 83

Appendix 7. The summary statistics for the manufacture of food products ind. ............... 87

Appendix 8. The summary statistics of the manufacture of chemicals and chemical products

industry ............................................................................................................................... 91

Appendix 9. The summary statistics for the manufacture of furniture industry ................ 95

Appendix 10. The summary statistics of the manufacture of machinery and equipment

industry ............................................................................................................................... 98

Appendix 11. Industry comparisons of the 5 industries .................................................... 102

Appendix 12. Differentiating the integration strategies for the whole sample ................. 107

Appendix 13. Concentration indices .................................................................................. 109

Appendix 14. Integration strategy comparison for the whole data .................................... 111

Appendix 15. Stata outputs for the manufacture of food industry by simple OLS ........... 113

Appendix 16. Stata output for the manufacture of food products industry by forward stepwise

regression with interactive terms ........................................................................................ 114

Appendix 17. Stata outputs for the manufacture of machinery and equipment industry by

simple OLS ......................................................................................................................... 118

Appendix 18. Stata output for the manufacture of machinery and equipment industry by

forward stepwise regression with interactive terms ........................................................... 119

Sele

n G

ül

The

Eff

ects

of I

nteg

ratio

n St

rate

gies

on

Firm

Per

form

ance

73

Appendix 1. Orbis snapshot and the search strategy

Data update

8619

Username

Aarhus Business School-3474

Export date

08/04/2011

1. World region/Country/Region in country: Denmark

395,183

2. NACE Rev. 2 (Primary codes only): 10 - Manufacture of food

1,617,959

products, 20 - Manufacture of chemicals and chemical products, 21

- Manufacture of basic pharm

aceutical products and pharm

aceutical

preparations, 28 - Manufacture of machinery and equipment nec, 31

- Manufacture of furniture

3. Years with available accounts: 2009, 2008, 2007, 2006, 2005

5,642,512

4. Number of employees: 2009, 2008, 2007, 2006, 2005, min=10, for all

the selected periods

628,275

5. Operating revenue (Turnover): All companies with a known value,

2009, 2008, 2007, 2006, 2005, for all the selected periods

3,473,365

Boolean Search: 1 And 2 And 3 And 4 And 5

TOTAL

158


74

Appendix 2. List of companies based on the industries.

Table 1: Manufacture of Basic Pharmaceuticals and Pharmaceutical Preparations

Pharmaceutical Industry- Companies 1. Novo Nordisk A/S 7. Xelia Pharmaceuticals ApS 2. H. Lundbeck A/S 8. Basf A/S 3. Novozymes A/S 9. Bavarian Nordic A/S 4. Leo Pharma A/S 10. Contura International A/S 5. Alk Abello A/S 11. Mekos Laboratories ApS 6. Nycomed Danmark ApS

Table 2: Manufacture of Chemicals and Chemical Products

Chemical Industry-Companies 1. Borealis Group 11. Aga A/S 2. Cheminova A/S 12. Trevira Neckelman ApS 3. Hempel A/S 13. Sun Chemical A/S 4. Dako Denmark A/S 14. Yara Praxair A/S 5. FiberVisions A/S 15. Flint Group Denmark A/S 6. Brenntag Nordic A/S 16. Syntese A/S 7. Koppers Denmark A/S 17. Basf Construction Chemicals Denmark A/S 8. Teknos A/S 18. Nordalim A/S 9. Danlind A/S 19. GK Pharma ApS 10. Air Liquide Danmark A/S

Table 3: Manufacture of Furniture

Furniture Industry-Companies 1. Tvilum ApS 9. Fredericia Furniture A/S 2. Dan-Foam ApS 10. Ropox A/S 3. Expedit A/S 11. Kvik Production A/S 4. Invita Kokkener A/S 12. P.P. Mobler ApS 5. Dansani A/S 13. Lystrup Rustfri Stal ApS 6. Labflex A/S 14. Solrod Mobel A/S 7. Duba-B8 A/S 15. Aktielskabet J.L. Mollers Mobelfabrik 8. JKE Design A/S


75

Table 4: Manufacture of Machinery and Equipment

Machinery Industry-Companies 1. Vestas Nacelles A/S 27. Tetra Pak Hoyer A/S 2. Vestas Blades A/S 28. Glunz & Jensen A/S 3. Vestas Towers A/S 29. CFS Slagelse A/S 4. Grundfos A/S 30. Epoke A/S 5. Vestas Control Systems A/S 31. HOH Water Technology A/S 6. LM Wind Power A/S 32. Kroll Cranes A/S 7. Sauer-Danfoss ApS 33. Dantherm Filtration AS 8. Gea Process Engineering A/S 34. Westrup A/S 9. Alfa Laval Copenhagen A/S 35. Vola A/S 10. Alfa Laval Kolding A/S 36. Soco System A/S 11. SPX Flow Technology Denmark A/S 37. Egholm Maskiner A/S 12. Kongskilde Industries A/S 38. Alfa Laval Nakskov A/S 13. Desmi A/S 39. Scanomat A/S 14. Sondex A/S 40. KJ Industries A/S 15. Hojbjerg Maskinfabrik A/S 41. Skako Lift A/S 16. Andritz Feed & Biofuel A/S 42. Serman & Tipsmark A/S 17. Disa Industries A/S 43. KSM Kragelund ApS 18. Wittenborg ApS 44. Heta A/S 19. Kverneland Group Kerteminde A/S 45. Acta A/S 20. Struers A/S 46. Boe-Therm A/S 21. Caljan Rite-Hite ApS 47. Abeto-Teknik A/S 22. SFK Systems A/S 48. Magnus Jensen A/S 23. Jensen Denmark A/S 24. Exhausto A/S 25. Gram Commercial A/S 26. Haas-Meincke A/S

Table 5: Manufacture of Food Products

Food Industry- Companies 1. Leverandorselskabet Danish Crown

Amba 28. Dan Cake A/S

2. Danisco A/S 29. Pharma Nord ApS 3. Royal Greenland Seafood A/S 30. Thorfisk A/S 4. Aarhuskarlshamn Denmark A/S 31. Valsemollen af 1899 A/S 5. Arovit Petfood A/S 32. Rahbekfisk A/S 6. Lantmannen Danpo A/S 33. Aktieselskabet Saby Fiske Industri 7. Toms Gruppen A/S 34. Cremo Ingredients A/S 8. Fiskernes Fiskeindustri Amba

Skagen 35. Daloon A/S

9. Ferrosan A/S 36. Odense Marcipan A/S 10. Lantmannen Schulstad A/S 37. Hanstholm Fiskemelsfabrik A/S 11. Rynkeby Foods A/S 38. Hjalmar Nielsen A/S 12. Lantmannen Cerealia A/S 39. Hamlet Protein A/S 13. Kohberg Brod A/S 40. Sydvestjydsk Pelsdyrfoder Amba 14. Kelsen Group A/S 41. Norager Mejeri A/S


76

15. Dragsbak A/S 42. Fodercentralen for Holstebro og Omegn Amba

16. Aktieselskabet Beauvais 43. Easyfood A/S 17. Bisca A/S 44. Credin A/S 18. Palsgaard A/S 45. Dangront Products A/S 19. CO-RO Food A/S 46. Agrana Juice Denmark A/S 20. Protein og Oliefabrikken Scanola

A/S 47. P/F Fiskavirkid

21. Haribo Lakrids, Aktieselskab 48. PK Chemicals A/S 22. Scandic Food A/S 49. Samso Konservesfabrik A/S 23. Rieber & Son Danmark A/S 50. European Freeze Dry ApS 24. Stryhns A/S 51. Sjallands Pelsdyrfoder Amba 25. Vital Petfood Group A/S 52. Aarhus Slagtehus A/S 26. Gumlink A/S 53. CP Kelco Services ApS 27. P/F Havsbrun 54. P/F Kosin

Sele

n G

ül

The

Eff

ects

of I

nteg

ratio

n St

rate

gies

on

Firm

Per

form

ance

77

Appendix 3. The Input-Output table from statbank.dk for the manufacture of food products industry

2005 2006 2007 2005

(perc.)

2006

(perc.)

2007

(perc.)

Avera

ge

Agr

icul

ture

-(Su

pply

ing)

34

54 6 35

22 1 36

30 6 0,

47

0,47

0,

48

0,47

Mfr

. of d

airy

pro

duct

s-(S

uppl

ying

) 39

08

4098

38

03

0,05

0,

05

0,05

0,

05

Who

lesa

le e

xcep

t of m

otor

veh

icle

s-(S

uppl

ying

) 32

00

3749

40

04

0,04

0,

05

0,05

0,

05

Prod

uctio

n et

c. o

f mea

t and

mea

t pro

duct

s-(S

uppl

ying

) 33

78

3207

29

47

0,05

0,

04

0,04

0,

04

Frei

ght t

rans

port

by

road

and

via

pip

elin

es-(

Supp

lyin

g)

2443

24

50

2312

0,

03

0,03

0,

03

0,03

A

dver

tisin

g-(S

uppl

ying

) 17

96

1692

20

48

0,02

0,

02

0,03

0,

02

Mfr

. of s

tarc

h, c

hoco

late

and

sug

ar p

rodu

cts-

(Sup

plyi

ng)

1524

16

73

1947

0,

02

0,02

0,

03

0,02

O

ther

bus

ines

s ac

tiviti

es-(

Supp

lyin

g)

1568

15

52

1439

0,

02

0,02

0,

02

0,02

M

fr. o

f veg

etab

le a

nd a

nim

al o

ils a

nd fa

ts-(

Supp

lyin

g)

1037

10

25

1320

0,

01

0,01

0,

02

0,01

Fi

shin

g-(S

uppl

ying

) 10

61

1097

11

49

0,01

0,

01

0,02

0,

01

Prod

uctio

n an

d di

stri

butio

n of

ele

ctri

city

-(Su

pply

ing)

93

6 10

78

1109

0,

01

0,01

0,

01

0,01

Pr

oces

sing

and

pre

serv

ing

of fi

sh a

nd fi

sh p

rodu

cts-

(Sup

plyi

ng)

951

1078

98

9 0,

01

0,01

0,

01

0,01

M

fr. o

f pul

p, p

aper

and

pap

er p

rodu

cts-

(Sup

plyi

ng)

1038

95

9 89

7 0,

01

0,01

0,

01

0,01

C

onsu

lting

eng

inee

rs, a

rchi

tect

s -(

Supp

lyin

g)

1016

95

8 90

5 0,

01

0,01

0,

01

0,01

M

anuf

actu

re o

f sug

ar-(

Supp

lyin

g)

1021

86

4 88

8 0,

01

0,01

0,

01

0,01

M

fr. o

f rub

ber p

rodu

cts

and

plas

tic p

acki

ng g

oods

etc

.-(Su

pply

ing)

82

6 85

8 88

6 0,

01

0,01

0,

01

0,01

Fi

nanc

ial i

nstit

utio

ns-(

Supp

lyin

g)

899

816

831

0,01

0,

01

0,01

0,

01

Man

ufac

ture

and

dis

trib

utio

n of

gas

-(Su

pply

ing)

75

7 84

6 74

2 0,

01

0,01

0,

01

0,01

Pr

oces

sing

and

pre

serv

ing

of fr

uit a

nd v

eget

able

s-(S

uppl

ying

) 74

6 73

3 64

3 0,

01

0,01

0,

01

0,01

B

uild

ing-

clea

ning

act

iviti

es-(

Supp

lyin

g)

646

553

586

0,01

0,

01

0,01

0,

01

Let

ting

of n

on-r

esid

entia

l bui

ldin

gs-(

Supp

lyin

g)

560

580

613

0,01

0,

01

0,01

0,

01

Post

and

tele

com

mun

icat

ions

-(Su

pply

ing)

58

0 60

6 53

1 0,

01

0,01

0,

01

0,01

M

fr. o

f var

ious

met

al p

rodu

cts-

(Sup

plyi

ng)

582

542

549

0,01

0,

01

0,01

0,

01

Man

ufac

ture

of b

ever

ages

-(Su

pply

ing)

50

8 57

6 52

4 0,

01

0,01

0,

01

0,01

Sele

n G

ül

The

Eff

ects

of I

nteg

ratio

n St

rate

gies

on

Firm

Per

form

ance

78

Act

iviti

es o

f oth

er tr

ansp

ort a

genc

ies-

(Sup

plyi

ng)

657

525

409

0,01

0,

01

0,01

0,

01

Mfr

. of p

harm

aceu

tical

s et

c.-(

Supp

lyin

g)

348

328

380

0,00

0,

00

0,00

0,

00

Sew

age

rem

oval

and

pur

ifyi

ng p

lant

s-(S

uppl

ying

) 37

3 33

7 34

0 0,

01

0,00

0,

00

0,00

M

fr. o

f bui

ldin

g m

ater

ials

of m

etal

-(Su

pply

ing)

32

2 31

2 34

4 0,

00

0,00

0,

00

0,00

R

epai

r and

mai

nten

ance

of b

uild

ings

-(Su

pply

ing)

32

4 27

3 30

9 0,

00

0,00

0,

00

0,00

M

fr. o

f det

erge

nts

and

othe

r che

mic

al p

rodu

cts-

(Sup

plyi

ng)

224

296

368

0,00

0,

00

0,00

0,

00

Soft

war

e co

nsul

tanc

y an

d su

pply

-(Su

pply

ing)

32

7 26

8 25

7 0,

00

0,00

0,

00

0,00

R

ecre

atio

nal,

cultu

ral,

spor

ting

activ

ities

(mar

ket)

-(Su

pply

ing)

27

2 30

2 24

4 0,

00

0,00

0,

00

0,00

N

on-l

ife

insu

ranc

e-(S

uppl

ying

) 26

9 27

4 20

1 0,

00

0,00

0,

00

0,00

M

fr. o

f ove

ns a

nd c

old-

stor

age

plan

ts-(

Supp

lyin

g)

232

247

265

0,00

0,

00

0,00

0,

00

Man

ufac

ture

of t

obac

co p

rodu

cts-

(Sup

plyi

ng)

243

258

225

0,00

0,

00

0,00

0,

00

Mfr

. of o

ther

ele

ctri

cal m

achi

nery

and

app

arat

us-(

Supp

lyin

g)

232

215

218

0,00

0,

00

0,00

0,

00

Com

pute

r act

iviti

es e

xc. s

oftw

are

cons

ulta

ncy

and

supp

ly-(

Supp

lyin

g)

229

208

216

0,00

0,

00

0,00

0,

00

Mor

tgag

e cr

edit

inst

itutio

ns-(

Supp

lyin

g)

283

192

173

0,00

0,

00

0,00

0,

00

Hor

ticul

ture

, orc

hard

s et

c.-(

Supp

lyin

g)

180

210

228

0,00

0,

00

0,00

0,

00

Con

stru

ctio

n m

ater

ials

for o

wn-

acco

unt r

epai

r-(S

uppl

ying

) 23

9 18

0 18

7 0,

00

0,00

0,

00

0,00

A

ctiv

ities

of m

embe

rshi

p or

gani

zatio

ns-(

Supp

lyin

g)

215

209

175

0,00

0,

00

0,00

0,

00

Ren

ting

of tr

ansp

ort e

quip

men

t and

mac

hine

ry-(

Supp

lyin

g)

218

202

174

0,00

0,

00

0,00

0,

00

Mfr

. of b

read

, cak

es a

nd b

iscu

its-(

Supp

lyin

g)

163

215

201

0,00

0,

00

0,00

0,

00

Mfr

. of m

achi

nery

for i

ndus

trie

s-(S

uppl

ying

) 16

4 16

8 20

4 0,

00

0,00

0,

00

0,00

R

esta

uran

ts -(

Supp

lyin

g)

172

181

172

0,00

0,

00

0,00

0,

00

Ref

use

colle

ctio

n an

d sa

nita

tion-

(Sup

plyi

ng)

173

181

137

0,00

0,

00

0,00

0,

00

Mfr

. of r

efin

ed p

etro

leum

pro

duct

s et

c.-(

Supp

lyin

g)

103

116

183

0,00

0,

00

0,00

0,

00

Col

lect

ion

and

dist

ribu

tion

of w

ater

-(Su

pply

ing)

12

7 12

4 13

7 0,

00

0,00

0,

00

0,00

A

ccou

ntin

g, b

ook-

keep

ing,

aud

iting

-(Su

pply

ing)

12

9 12

7 12

7 0,

00

0,00

0,

00

0,00

M

fr. o

f agr

icul

tura

l m

achi

nery

-(Su

pply

ing)

13

7 11

6 12

2 0,

00

0,00

0,

00

0,00

C

argo

han

dlin

g, h

arbo

urs

etc.

, tra

vel a

genc

ies-

(Sup

plyi

ng)

130

115

107

0,00

0,

00

0,00

0,

00

Mfr

. of t

rans

port

equ

ipm

ent e

xcl.

ship

s, m

otor

veh

icle

s et

c.-(

Supp

lyin

g)

103

128

118

0,00

0,

00

0,00

0,

00

Civ

il en

gine

erin

g-(S

uppl

ying

) 11

7 88

99

0,

00

0,00

0,

00

0,00

Sele

n G

ül

The

Eff

ects

of I

nteg

ratio

n St

rate

gies

on

Firm

Per

form

ance

79

Tra

nspo

rt v

ia ra

ilway

s-(S

uppl

ying

) 13

1 93

79

0,

00

0,00

0,

00

0,00

H

otel

s-(S

uppl

ying

) 10

1 10

3 87

0,

00

0,00

0,

00

0,00

M

aint

enan

ce a

nd re

pair

of m

otor

veh

icle

s-(S

uppl

ying

) 92

97

10

0 0,

00

0,00

0,

00

0,00

R

eal e

stat

e ag

ents

etc

.-(Su

pply

ing)

89

84

76

0,

00

0,00

0,

00

0,00

Pu

blis

hing

act

iviti

es, e

xclu

ding

new

spap

ers-

(Sup

plyi

ng)

94

78

65

0,00

0,

00

0,00

0,

00

Stea

m a

nd h

ot w

ater

sup

ply-

(Sup

plyi

ng)

92

93

49

0,00

0,

00

0,00

0,

00

Man

ufac

ture

of o

ther

pla

stic

pro

duct

s n.

e.c.

-(Su

pply

ing)

77

75

80

0,

00

0,00

0,

00

0,00

O

ther

ser

vice

act

iviti

es-(

Supp

lyin

g)

77

71

80

0,00

0,

00

0,00

0,

00

Prin

ting

activ

ities

-(Su

pply

ing)

76

68

78

0,

00

0,00

0,

00

0,00

R

efus

e du

mps

and

refu

se d

ispo

sal p

lant

s-(S

uppl

ying

) 82

78

60

0,

00

0,00

0,

00

0,00

O

ther

reta

il sa

le, r

epai

r wor

k-(S

uppl

ying

) 66

67

74

0,

00

0,00

0,

00

0,00

M

fr. o

f woo

d an

d w

ood

prod

ucts

-(Su

pply

ing)

59

61

65

0,

00

0,00

0,

00

0,00

R

etai

l tra

de o

f foo

d -(

Supp

lyin

g)

57

62

64

0,00

0,

00

0,00

0,

00

Leg

al a

ctiv

ities

-(Su

pply

ing)

62

65

54

0,

00

0,00

0,

00

0,00

M

fr. o

f bas

ic n

on-f

erro

us m

etal

s-(S

uppl

ying

) 59

59

54

0,

00

0,00

0,

00

0,00

Pu

blis

hing

of n

ewsp

aper

s-(S

uppl

ying

) 47

57

57

0,

00

0,00

0,

00

0,00

M

fr. o

f rad

io a

nd c

omm

unic

atio

n eq

uipm

ent-

(Sup

plyi

ng)

40

35

69

0,00

0,

00

0,00

0,

00

Gen

eral

(ove

rall)

pub

lic s

ervi

ce a

ctiv

ities

-(Su

pply

ing)

56

42

35

0,

00

0,00

0,

00

0,00

M

fr. o

f ind

ustr

ial g

ases

and

inor

gani

c ba

cis

chem

ical

s-(S

uppl

ying

) 42

44

40

0,

00

0,00

0,

00

0,00

M

fr. o

f off

ice

mac

hine

ry a

nd c

ompu

ters

-(Su

pply

ing)

55

36

35

0,

00

0,00

0,

00

0,00

A

ctiv

ities

aux

iliar

y to

fina

nce-

(Sup

plyi

ng)

40

46

39

0,00

0,

00

0,00

0,

00

Mfr

. of m

arin

e en

gine

s an

d co

mpr

esso

rs -(

Supp

lyin

g)

33

41

30

0,00

0,

00

0,00

0,

00

Ext

r. of

gra

vel a

nd c

lay

etc.

-(Su

pply

ing)

31

41

24

0,

00

0,00

0,

00

0,00

T

axi o

pera

tion

and

coac

h se

rvic

es-(

Supp

lyin

g)

34

31

29

0,00

0,

00

0,00

0,

00

Adm

inis

trat

ion

of p

ublic

sec

tors

exc

. for

bis

ines

s-(S

uppl

ying

) 31

34

26

0,

00

0,00

0,

00

0,00

M

fr. o

f med

ical

and

opt

ical

inst

rum

ents

-(Su

pply

ing)

28

33

28

0,

00

0,00

0,

00

0,00

R

ecyc

ling

of w

aste

and

scr

ap-(

Supp

lyin

g)

24

31

25

0,00

0,

00

0,00

0,

00

Def

ence

, pol

ice

and

adm

inis

trat

ion

of ju

stic

e-(S

uppl

ying

) 17

18

44

0,

00

0,00

0,

00

0,00

A

ir tr

ansp

ort-

(Sup

plyi

ng)

24

21

26

0,00

0,

00

0,00

0,

00

Sele

n G

ül

The

Eff

ects

of I

nteg

ratio

n St

rate

gies

on

Firm

Per

form

ance

80

Man

ufac

ture

of m

otor

veh

icle

s et

c.-(

Supp

lyin

g)

18

23

27

0,00

0,

00

0,00

0,

00

Sale

of m

otor

veh

icle

s an

d m

otor

cycl

es-(

Supp

lyin

g)

19

22

26

0,00

0,

00

0,00

0,

00

Ret

ail s

ale

of a

utom

otiv

e fu

el-(

Supp

lyin

g)

16

20

19

0,00

0,

00

0,00

0,

00

Dep

artm

ent s

tore

s-(S

uppl

ying

) 15

16

18

0,

00

0,00

0,

00

0,00

O

ther

sch

edul

ed p

asse

nger

land

tran

spor

t-(S

uppl

ying

) 17

16

15

0,

00

0,00

0,

00

0,00

M

fr. o

f tex

tiles

-(Su

pply

ing)

18

13

15

0,

00

0,00

0,

00

0,00

M

anuf

actu

re o

f pes

ticid

es a

nd o

ther

agr

o-ch

emic

al p

rodu

cts-

(Sup

plyi

ng)

13

16

16

0,00

0,

00

0,00

0,

00

Mfr

. of c

oncr

ete,

cem

ent,

asph

alt a

nd ro

ckw

ool p

rodu

cts-

(Sup

plyi

ng)

14

14

17

0,00

0,

00

0,00

0,

00

Mfr

. of d

yes,

pig

men

ts a

nd o

rgan

ic b

acis

che

mic

als-

(Sup

plyi

ng)

23

14

6 0,

00

0,00

0,

00

0,00

M

fr. o

f gla

ss a

nd c

eram

ic g

oods

etc

.-(Su

pply

ing)

8

9 25

0,

00

0,00

0,

00

0,00

W

ater

tran

spor

t-(S

uppl

ying

) 12

13

14

0,

00

0,00

0,

00

0,00

M

fr. o

f cem

ent,

bric

ks, t

iles,

flag

s et

c.-(

Supp

lyin

g)

14

15

7 0,

00

0,00

0,

00

0,00

A

dult

and

othe

r edu

catio

n (m

arke

t)-(

Supp

lyin

g)

14

13

10

0,00

0,

00

0,00

0,

00

Reg

ulat

ion

of a

nd c

ontr

ibut

ion

to m

ore

effi

cien

t ope

ratio

n of

bus

ines

s-(S

uppl

ying

) 15

11

11

0,

00

0,00

0,

00

0,00

Mfr

. of t

oys,

gol

d an

d si

lver

art

icle

s et

c.-(

Supp

lyin

g)

12

13

10

0,00

0,

00

0,00

0,

00

Adu

lt an

d ot

her e

duca

tion

(oth

er n

on-m

arke

t)-(

Supp

lyin

g)

13

13

7 0,

00

0,00

0,

00

0,00

M

fr. o

f bui

lder

s w

are

of p

last

ic-(

Supp

lyin

g)

10

8 10

0,

00

0,00

0,

00

0,00

R

e. s

ale

of p

har.

good

s, c

osm

etic

art

.-(Su

pply

ing)

10

8

8 0,

00

0,00

0,

00

0,00

M

fr. o

f fur

nitu

re-(

Supp

lyin

g)

8 10

7

0,00

0,

00

0,00

0,

00

Res

earc

h an

d de

velo

pmen

t (m

arke

t)-(

Supp

lyin

g)

8 8

9 0,

00

0,00

0,

00

0,00

M

fr. o

f pai

nts,

var

nish

es a

nd s

imila

r coa

tings

, pri

ntin

g in

k an

d m

astic

s-(S

uppl

ying

) 8

5 6

0,00

0,

00

0,00

0,

00

Mfr

. of d

omes

tic a

pplia

nces

-(Su

pply

ing)

6

6 5

0,00

0,

00

0,00

0,

00

Firs

t pro

cess

ing

of ir

on a

nd s

teel

-(Su

pply

ing)

3

5 8

0,00

0,

00

0,00

0,

00

Mfr

. of p

last

ics

and

synt

hetic

rubb

er-(

Supp

lyin

g)

10

4 3

0,00

0,

00

0,00

0,

00

Mfr

. of w

eari

ng a

ppar

el-(

Supp

lyin

g)

5 5

3 0,

00

0,00

0,

00

0,00

C

astin

g of

met

al p

rodu

cts-

(Sup

plyi

ng)

2 2

4 0,

00

0,00

0,

00

0,00

H

ighe

r edu

catio

n-(S

uppl

ying

) 2

2 1

0,00

0,

00

0,00

0,

00

Sele

n G

ül

The

Eff

ects

of I

nteg

ratio

n St

rate

gies

on

Firm

Per

form

ance

81

*Whe

n th

e se

cond

ary

NA

CE

Rev

. cod

e w

as d

iffe

rent

than

the

prim

ary

code

of t

he fi

rm, v

ertic

al in

tegr

atio

n ha

s be

en tr

aced

from

this

IO m

atri

x.

Bas

ed o

n th

e de

fini

tion

of th

e se

cond

ary

NA

CE

cod

e, th

e su

pply

ing

indu

stry

was

sea

rche

d fr

om th

e lis

t. If

the

aver

age

perc

enta

ge e

xcee

ded

1%

thre

shol

d, th

e co

mpa

ny is

con

side

red

to b

e ve

rtic

ally

inte

grat

ed. I

f not

, the

com

pany

is u

nrel

ated

div

ersi

fied

.

Man

ufac

ture

of f

ertil

izer

s-(S

uppl

ying

) 2

1 2

0,00

0,

00

0,00

0,

00

Bak

ers

shop

s-(S

uppl

ying

) 1

1 1

0,00

0,

00

0,00

0,

00

Bui

ldin

g an

d re

pair

ing

of s

hips

and

boa

ts-(

Supp

lyin

g)

1 2

1 0,

00

0,00

0,

00

0,00

R

esea

rch

and

deve

lopm

ent (

othe

r non

-mar

ket)

-(Su

pply

ing)

1

1 1

0,00

0,

00

0,00

0,

00

Mfr

. of b

asic

iron

and

ste

el a

nd o

f fer

ro a

lloys

-(Su

pply

ing)

1

1 2

0,00

0,

00

0,00

0,

00

Med

ical

, den

tal a

nd v

eter

inar

y ac

tiviti

es-(

Supp

lyin

g)

1 1

0 0,

00

0,00

0,

00

0,00

Fo

rest

ry-(

Supp

lyin

g)

1 1

1 0,

00

0,00

0,

00

0,00

A

gric

ultu

ral s

ervi

ces;

land

scap

e ga

rden

ers

etc.

-(Su

pply

ing)

0

0 0

0,00

0,

00

0,00

0,

00

Mfr

. of l

eath

er a

nd fo

otw

ear-

(Sup

plyi

ng)

0 0

0 0,

00

0,00

0,

00

0,00

R

e. s

ale

of c

loth

ing

and

foot

wea

r-(S

uppl

ying

) 0

0 0

0,00

0,

00

0,00

0,

00

Ext

r. of

oil

and

nat

ural

gas

-(Su

pply

ing)

0

0 0

0,00

0,

00

0,00

0,

00

Con

stru

ctio

n of

new

bui

ldin

gs-(

Supp

lyin

g)

0 0

0 0,

00

0,00

0,

00

0,00

L

ife

insu

ranc

e an

d pe

nsio

n fu

ndin

g-(S

uppl

ying

) 0

0 0

0,00

0,

00

0,00

0,

00

Dw

ellin

gs-(

Supp

lyin

g)

0 0

0 0,

00

0,00

0,

00

0,00

Pr

imar

y ed

ucat

ion-

(Sup

plyi

ng)

0 0

0 0,

00

0,00

0,

00

0,00

Se

cond

ary

educ

atio

n-(S

uppl

ying

) 0

0 0

0,00

0,

00

0,00

0,

00

Hos

pita

l act

iviti

es-(

Supp

lyin

g)

0 0

0 0,

00

0,00

0,

00

0,00

So

cial

inst

itutio

ns e

tc. f

or c

hild

ren-

(Sup

plyi

ng)

0 0

0 0,

00

0,00

0,

00

0,00

So

cial

inst

itutio

ns e

tc. f

or a

dults

-(Su

pply

ing)

0

0 0

0,00

0,

00

0,00

0,

00

Rec

reat

iona

l, cu

ltura

l, sp

ortin

g ac

tiviti

es (o

ther

non

-mar

ket)

-(Su

pply

ing)

0

0 0

0,00

0,

00

0,00

0,

00

Priv

ate

hous

ehol

ds w

ith e

mpl

oyed

per

sons

-(Su

pply

ing)

0

0 0

0,00

0,

00

0,00

0,

00

TO

TA

L S

UPP

LY (i

ndus

try

10)

7426 8

7518 7

7618 0

81

Appendix 4. A representation on how the integration strategies have been identified (Example from the chemicals industry)

Table 6: Manufacture of Chemicals and Chemical Products Industry

*As seen in the table above, if the company has revealed only its primary code as in Hempel A/S, the firm is regarded as being un-diversified. **If the first two digit primary & secondary codes are the same, we will take them as horizontally integrated (HI=1). ***If not, as in the third case, we will investigate vertical integration with the use of the IO matrix based on the definition of the secondary NACE code (23). The percentage level where the two industries are intercepting will give an idea of VI. For this, a minimum percentage index has to identified and based on common sense and 10 first biggest suppliers, an index of 1% is to be chosen. If NACE 23 is not a supplying industry (below 1%) for the chemicals industry, then the company is regarded as unrelated diversified. Here, the analysis is based on the priority level of the first secondary NACE code that is presented; therefore the code 4676 is not taken into consideration when identifying FiberVision A/S’s integration strategy. This assumption is to preserve the mutual exclusivity of the integration strategies.

Company Core Code Secondary Code Hempel A/S* 2030 -- BOREALIS Group** 2016 2059 FiberVisions A/S*** 2060 2365

4676

82

Appendix 5: The return on assets value for Novo Nordisk A/S through the years 2005-2009.

Table 7: The first 3 companies’ ROA values from the pharmaceutical industry

Companies 2009 2008 2007 2006 2005 Novo Nordisk A/S* 7.03 9.42 4.63 6.05 n.a H. Lundbeck A/S 15.57 16.84 20.78 14.04 19.40 Novozymes A/S 14.88 14.30 15.61 15.29 15.73 *The ROA values for Novo Nordisk A/S are not representing the success and the profitability of the company, compared to the other following firms. When the operating revenue and net income per employee figures are used, it is observed that Novo Nordisk A/S has the highest measures as shown in Table7.

Table 8: The first 3 companies’ average operating revenue per employee values from the pharmaceutical industry

Companies 2009 2008 2007 2006 2005 Novo Nordisk A/S* 2,551.24 2,538.58 2,499.33 2,567.76 n.a H. Lundbeck A/S 2,397.87 2,126.27 2,162.72 1,788.26 1,803.33 Novozymes A/S 1,631.59 1,640.90 1,614.22 1,606.04 1,569.72

Table 9: The first 3 companies’ ROA values from the food industry

Companies 2009 2008 2007 2006 2005 Danish Crown Amba* 5.93 5.12 6.62 6.36 6.00 Danisco A/S 1.79 4.90 4.93 2.77 5.16 Royal Greenland A/S -6.82 -3.09 1.28 -3.00 1.04 *The same reasoning can be used here, that the ROA values are very low which are far beyond the profitabilities and leadership positions of the companies. The average operating revenue per employee figures presented in Table 9 are more reasonable and reflecting the successes of the firms.

Table 10: The first 3 companies’ average operating revenue per employee values from the food industry

Companies 2009 2008 2007 2006 2005 Danish Crown Amba 1,844.17 1,762.42 1,822.38 1,801.69 1,702.04 Danisco A/S 1,865.41 2,059.66 1,994.7 2,058.93 1,688.36 Royal Greenland A/S* 12,874.28 9,189.45 8,555.43 8,210.50 5,342.57 *Royal Greenland Seafood A/S has high operating revenue per employee values due to having lower number of employees compared to the other firms.

83

Appendix 6. The summary statistics of the manufacture of basic pharmaceutical products and pharmaceutical preparations industry

Table 11: The pharmaceutical companies based on integration strategies

Pharmaceutical Industry- Companies’ Integration Strategies 1. Novo Nordisk A/S UR 7. Xelia Pharmaceuticals

ApS HI

2. H. Lundbeck A/S VI 8. Basf A/S UR 3. Novozymes A/S UR 9. Bavarian Nordic A/S VI 4. Leo Pharma A/S UD 10. Contura International A/S UD 5. Alk Abello A/S VI 11. Mekos Laboratories ApS UD 6. Nycomed Danmark ApS VI UR= Unrelated Diversified VI= Vertical Integration HI= Horizontal Integration UD= Undiversified

Output 1. Summary statistics

84

Output 2. Detailed summary statistics

86

Output 3. Correlations

Output 4. Sample histograms for highly skewed values (pharmaceutical industry)

Capital Intensity: Cost per Employee:

Market Share: Ratio:

05.0e-05

1.0e-04

1.5e-04

Density

0 5000 10000 15000Capital Intensity (fixed assets/employees

0.001

.002

.003

.004

.005

Density

400 500 600 700 800Average Cost per empl.

01

23

45

Density

0 .1 .2 .3 .4Average Market Shares

0.01

.02

.03

Density

20 40 60 80Average Cost of empl./opr. Rev per empl.

87

Appendix 7. The summary statistics of the manufacture of food products industry

Table 12: The manufacture of food industry companies based on integration strategies

Food Industry- Companies’ Integration Strategies 1. Leverandorselskabet Danish

Crown Amba HI 28. Dan Cake A/S HI

2. Danisco A/S UR 29. Pharma Nord ApS UD 3. Royal Greenland Seafood A/S VI 30. Thorfisk A/S UD 4. Aarhuskarlshamn Denmark A/S UR 31. Valsemollen af 1899 A/S UD 5. Arovit Petfood A/S UD 32. Rahbekfisk A/S HI 6. Lantmannen Danpo A/S HI 33. Aktieselskabet Saby Fiske

Industri UD

7. Toms Gruppen A/S UD 34. Cremo Ingredients A/S UD 8. Fiskernes Fiskeindustri Amba

Skagen UD 35. Daloon A/S UD

9. Ferrosan A/S UR 36. Odense Marcipan A/S UD 10. Lantmannen Schulstad A/S UD 37. Hanstholm Fiskemelsfabrik

A/S HI

11. Rynkeby Foods A/S HI 38. Hjalmar Nielsen A/S VI 12. Lantmannen Cerealia A/S VI 39. Hamlet Protein A/S UD 13. Kohberg Brod A/S VI 40. Sydvestjydsk Pelsdyrfoder

Amba HI

14. Kelsen Group A/S HI 41. Norager Mejeri A/S UD 15. Dragsbak A/S HI 42. Fodercentralen for Holstebro

og Omegn Amba UD

16. Aktieselskabet Beauvais HI 43. Easyfood A/S UD 17. Bisca A/S HI 44. Credin A/S VI 18. Palsgaard A/S UD 45. Dangront Products A/S HI 19. CO-RO Food A/S UR 46. Agrana Juice Denmark A/S UD 20. Protein og Oliefabrikken Scanola

A/S HI 47. P/F Fiskavirkid UD

21. Haribo Lakrids, Aktieselskab VI 48. PK Chemicals A/S HI 22. Scandic Food A/S HI 49. Samso Konservesfabrik A/S UD 23. Rieber & Son Danmark A/S HI 50. European Freeze Dry ApS UD 24. Stryhns A/S HI 51. Sjallands Pelsdyrfoder Amba UD 25. Vital Petfood Group A/S UD 52. Aarhus Slagtehus A/S UD 26. Gumlink A/S HI 53. CP Kelco Services ApS HI 27. P/F Havsbrun HI 54. P/F Kosin UD

88



90


91

Appendix 8: The summary statistics of the manufacture of chemicals and chemical products industry

Table 13: The manufacture of chemicals and chemical products industry companies based on integration strategies

Chemical Industry-Companies’ Integration Strategies 1. Borealis Group HI 11. Aga A/S VI 2. Cheminova A/S HI 12. Trevira Neckelman ApS UD 3. Hempel A/S UR 13. Sun Chemical A/S UR 4. Dako Denmark A/S UD 14. Yara Praxair A/S VI 5. FiberVisions A/S UR 15. Flint Group Denmark A/S UD 6. Brenntag Nordic A/S HI 16. Syntese A/S UD 7. Koppers Denmark A/S HI 17. Basf Construction Chemicals

Denmark A/S UR

8. Teknos A/S HI 18. Nordalim A/S UD 9. Danlind A/S UR 19. GK Pharma ApS UD 10. Air Liquide Danmark A/S HI


92


94


95

Appendix 9. The summary statistics of the manufacture of furniture industry

Table 14: The manufacture of furniture industry companies based on integration strategies

Furniture Industry-Companies’ Integration Strategies 1. Tvilum ApS HI 9. Fredericia Furniture A/S VI 2. Dan-Foam ApS UD 10. Ropox A/S UD 3. Expedit A/S VI 11. Kvik Production A/S UD 4. Invita Kokkener A/S UR 12. P.P. Mobler ApS UD 5. Dansani A/S VI 13. Lystrup Rustfri Stal ApS UD 6. Labflex A/S UR 14. Solrod Mobel A/S UD 7. Duba-B8 A/S VI 15. Aktielskabet J.L. Mollers

Mobelfabrik UD

8. JKE Design A/S HI UD



97


98

Appendix 10: The summary statistics of the manufacture of machinery and equipment industry

Table 15: The manufacture of machinery and equipment industry companies based on integration strategies

Machinery Industry-Companies’ Integration Strategies 1. Vestas Nacelles A/S UD 27. Tetra Pak Hoyer A/S HI 2. Vestas Blades A/S UD 28. Glunz & Jensen A/S VI 3. Vestas Towers A/S UD 29. CFS Slagelse A/S UR 4. Grundfos A/S UD 30. Epoke A/S VI 5. Vestas Control Systems

A/S UD 31. HOH Water

Technology A/S UR

6. LM Wind Power A/S UD 32. Kroll Cranes A/S VI 7. Sauer-Danfoss ApS VI 33. Dantherm Filtration AS UR 8. Gea Process Engineering

A/S UD 34. Westrup A/S HI

9. Alfa Laval Copenhagen A/S

UD 35. Vola A/S UR

10. Alfa Laval Kolding A/S UR 36. Soco System A/S HI 11. SPX Flow Technology

Denmark A/S UR 37. Egholm Maskiner A/S UR

12. Kongskilde Industries A/S HI 38. Alfa Laval Nakskov A/S

VI

13. Desmi A/S HI 39. Scanomat A/S UR 14. Sondex A/S UD 40. KJ Industries A/S HI 15. Hojbjerg Maskinfabrik

A/S HI 41. Skako Lift A/S UR

16. Andritz Feed & Biofuel A/S

UD 42. Serman & Tipsmark A/S

HI

17. Disa Industries A/S UR 43. KSM Kragelund ApS UR 18. Wittenborg ApS UD 44. Heta A/S VI 19. Kverneland Group

Kerteminde A/S UD 45. Acta A/S VI

20. Struers A/S UR 46. Boe-Therm A/S HI 21. Caljan Rite-Hite ApS UD 47. Abeto-Teknik A/S HI 22. SFK Systems A/S VI 48. Magnus Jensen A/S VI 23. Jensen Denmark A/S VI 24. Exhausto A/S UD 25. Gram Commercial A/S UD 26. Haas-Meincke A/S VI

99



101


102

Appendix 11. Industry comparisons of the 5 industries

Graph 1: Companies by industries

Graph 2: Average operating revenue per employee by industries

Graph 3: Average net income per employee by industries

Pharmaceutical Industry

7%

Food Industry

37%

Chemical Industry

13%

Furniture Industry

10%

Machinery Industry

33%

Number of Companies by Industry

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

Industries 1,948.10 3,701.22 3,095.31 1,420.78 1,749.71

0.00500.00

1,000.001,500.002,000.002,500.003,000.003,500.004,000.00

Ope

rati

ng R

ev. p

er e

mpl

.

Average Operating Rev. per empl.


Food Industry

Chemical

Industry

Furniture

Industry

Machinery

Industry

Average N.I. per empl. 496.13 73.78 125.67 120.59 71.62

0.00100.00200.00300.00400.00500.00600.00

Net

inco

me

per e

mpl

.

Average Net Income per empl.

103

Graph 4: Average number of countries by industries

Graph 5: Average market shares by industries

Table 8: The sign of correlations among the variables

Corr. Pharma. Ind. Food Ind. Chemicals Ind. Furniture Ind. Machinery Ind. ORPE NIPE ORPE NIPE ORPE NIPE ORPE NIPE ORPE NIPE RISK - - - + - - + - - - SIZE + - + + + - - - - - CINT - + + + + + + + + + MARS + + - - + + + + + + CPE + - + + + + + + + + RATIO - - - + - - - - - - VI - + + - - + - - - - HI + + + + + + + - - - UR + - - + - - + - - + UD - - - - - - - + + + COUNTRY + + - - - - + + + +

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture

Industry

Machinery

Industry

Average Num. Of Countries 17 3 6 2 3

0

5

10

15

20

Num

ber o

f Cou

ntri

es

Average Num. of Countries

Pharmaceutical

Industry* (4 years)

Food Industry

Chemical Industry

Furniture Industry

Machinery

Industry

Average Market Share 9.04% 1.77% 4.66% 5.66% 2.03%

0.00%

2.00%4.00%6.00%

8.00%10.00%

Mar

ket S

hare

Average Market Share

104

Graph 6: The pharmaceutical industry- operating revenue per employee and net income per employee comparison

Analysis VI HI UnRe. UnDiv. Average ORPE 1482,92 2247,45 2754,01 1662,66 Average NIPE 668,86 1053,20 450,92 125,34 Average MARS 0,08 0,02 0,22 0,04 Average COUNTRY 16 4 31 9

Graph 7: The food industry- operating revenue per employee and net income per employee comparison


VI HI UnRe. UnDiv.

Average ORPE 1482.92 2247.45 2754.01 1662.66

Average NIPE 668.86 1053.20 450.92 125.34

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

Perf

orm

ance

Val

ues


VI HI UnRe. UnDiv.

Average ORPE 4182.67 5016.02 3539.98 2724.06

Average NIPE 22.63 78.82 194.05 36.55

0.00

1000.00

2000.00

3000.00

4000.00

5000.00

6000.00

Perf

orm

ance

Val

ues

Food Industry

105

Graph 8: The chemicals industry- operating revenue per employee and net income per employee comparison


Graph 9: The furniture industry- operating revenue per employee and net income per employee comparison

VI HI UnRe. UnDiv.

Average ORPE 2432.59 4132.93 2174.22 3046.18

Average NIPE 297.63 222.02 87.89 3.47

0.00500.00

1000.001500.002000.002500.003000.003500.004000.004500.00

Perf

orm

ance

Val

ues

Chemicals Industry

VI HI UnRe. UnDiv.

Average ORPE 1372.38 1603.82 1537.20 1362.87

Average NIPE 32.41 60.02 -124.85 258.41

-400.00-200.00

0.00200.00400.00600.00800.00

1000.001200.001400.001600.001800.00

Perf

orm

ance

Val

ues

Furniture Industry

106

Analysis VI HI UnRe. UnDiv. Average ORPE 1372,38 1603,82 1537,20 1362,87 Average NIPE 32,41 60,02 -124,85 258,41 Average MARS 0,041 0,19 0,05 0,027 Average COUNTRY 3 1 2 3

Graph 10: The machinery and equipment industry- operating revenue per employee and net income per employee comparison

VI HI UnRe. UnDiv.

Average ORPE 1485.90 1002.55 1527.34 2619.16

Average NIPE 20.22 -43.26 157.97 116.82

-500.00

0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

Perf

orm

ance

Val

ues

Machinery Industry

Analysis VI HI UnRe. UnDiv. Average ORPE 1485,90 1002,55 1527,34 2619,16 Average NIPE 20,22 -43,26 157,97 116,82 Average MARS 0,010 0,006 0,009 0,047 Average COUNTRY 1 4 2 4

107

Appendix 12. Differentiating the integration strategies for the whole sample

Graph 11: Vertical integration

Graph 12: Horizontal integration

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

Average ORPE 1,482.92 4,182.67 2,432.59 1,372.38 1,485.90

Average NIPE 668.86 22.63 297.63 32.41 20.22

0.00500.00

1,000.001,500.002,000.002,500.003,000.003,500.004,000.004,500.00

Pref

orm

ance

Val

ues

VI by industry

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

Average ORPE 2,247.45 5,016.02 4,132.93 1,603.82 1,002.55

Average NIPE 1,053.20 78.82 222.02 60.02 -43.26

-1,000.000.00

1,000.002,000.003,000.004,000.005,000.006,000.00

Perf

orm

ance

Val

ues

HI by industry

108

Graph 13: Unrelated diversification strategy

Graph 14: Un-diversification strategy

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

Average ORPE 2,754.01 3,539.98 2,174.22 1,537.20 1,527.34

Average NIPE 450.92 194.05 87.89 -124.85 157.97

-500.000.00

500.001,000.001,500.002,000.002,500.003,000.003,500.004,000.00

Perf

orm

ance

Vla

ues

UR by industry

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

Average ORPE 1,662.66 2,724.06 3,046.18 1,362.87 2,619.16

Average NIPE 125.34 36.55 3.47 258.41 116.82

0.00

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

Perf

orm

ance

Val

ues

UD by industry

109

Appendix 13. Concentration indices

Graph 15: Herfindahl index

Graph 16: Entropy measure

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

Average HH index 0.3011 0.2552 0.4373 0.1641 0.1141

0.00000.05000.10000.15000.20000.25000.30000.35000.40000.45000.5000

HH

Inde

x

Average HH index

Pharmaceutical

Industry

Food Industry

Chemical Industry

Furniture Industry

Machinery

Industry

Average Entropy Measure 1.4888 2.1037 1.1176 1.7303 2.7795

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

E M

easu

re

Average Entropy Measure

110

Graph 17: Concentration ratio (CR4)

Graph 18: Relative measure


Food Industry

Chemical Industry

Furniture Industry

Machinery Industry

CR4 Ratio 0.92 0.69 0.81 0.64 0.53

0.000.100.200.300.400.500.600.700.800.901.00

CR4

Rati

o

Average CR4 Ratio

Pharmaceutical

Industry

Food Industry

Chemical

Industry

Furniture

Industry

Machinery

Industry

Average Relative Measure 0.1353 0.0397 0.0588 0.1154 0.0591

0.00000.02000.04000.06000.08000.10000.12000.14000.1600

RE M

easu

re

Average Relative Measure

111

Appendix 14. Integration strategy comparison for the whole data

Graph 19: Average operating revenue per employee

Graph 20: Average net income per employee

VI HI UR UD

Average O.R. Per empl. 2,138.05 3,443.85 2,335.34 2,495.30

0.00500.00

1,000.001,500.002,000.002,500.003,000.003,500.004,000.00

Ope

rati

ng R

ev. p

er e

mpl

oyee

Average Operating Revenue per Employee

VI HI UR UD

Average N.I. Per empl. 139.21 95.26 167.25 88.86

0.0020.0040.0060.0080.00

100.00120.00140.00160.00180.00

Net

Inco

me

per e

mpl

oyee

Average Net Income per Employee

112

Graph 21: Average market share

Graph 22: Average number of countries

VI HI UR UD

Average Market Share 2.33% 5.12% 3.82% 2.13%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

Mar

ket S

hare

Average Market Share

VI HI UR UD

Average Num. Of Countries 4.04 3.31 7.55 3.37

0.001.002.003.004.005.006.007.008.00

Num

ber o

f Cou

ntir

es

Average Num. Of Countries

113

Appendix 15. Stata outputs for the manufacture of food products industry by simple OLS

Output 17. Regression output for ORPE as the dependent variable

Output 18. Regression output for NIPE as the dependent variable

114

Appendix 16. Stata output for the manufacture of food products industry by forward stepwise regression with interactive terms.


115

Output 20. Regression output with interaction effects by simple OLS (ORPE as the dependent variable)

116


117

Output 22. Regression output with interaction effects by simple OLS (NIPE as the dependent variable)

118

Appendix 17: Stata output for the manufacture of machinery and equipment industry by simple OLS



119

Appendix 18: Stata output for the manufacture of machinery and equipment industry by forward stepwise regression with interactive terms.


120


121


122


The Effects of Integration Strategies on Firm Performance...

Documents

Transcript of The Effects of Integration Strategies on Firm Performance...